Racial Diversity, Electoral Preferences, and the Supply of Policy:

The Great Migration and Civil Rights∗

Alvaro Calderon† Vasiliki Fouka‡ Marco Tabellini§

October 2020

Abstract

Between 1940 and 1970, during the second Great Migration, more than 4 million AfricanAmericans moved from the South to the North of the United States. The Great Migrationcoincided with the rise of the civil rights movement, whose success marked a key turningpoint in the history of race relations in the US. In this paper, we show that these twoevents are causally linked. Predicting Black in-migration with a version of the shift-shareinstrument, we find that the Great Migration increased support for the Democratic Partyand encouraged pro-civil rights activism, among both Black and (some) white voters. Wealso show that Black in-migration induced northern Congress members to more activelypromote civil rights legislation. However, these average effects mask a steep rise in polar-ization. While the 1940s saw the replacement of moderate Republicans with increasinglyliberal Democrats, the 1950s were characterized by a right-wing shift within the GOP. Over-all, our findings suggest that, under certain conditions, cross-race coalitions can emerge, butalso that higher support for racial equality may coincide with higher political polarization.

Keywords: Race, diversity, civil rights, Great MigrationJEL Classification: D72, J15, N92

∗We are extremely grateful to Nicola Gennaioli and Jim Snyder for several insightful suggestions and formany constructive conversations. We also thank Alberto Alesina, Leah Boustan, Melissa Dell, Ryan Enos, JeffFrieden, Luigi Guiso, Petra Moser, Markus Nagler, Gerard Padro i Miquel, John Parman, Torsten Persson,Vincent Pons, Evan Taylor, Gaspare Tortorici, Nico Voigtlaender, Matt Weinzierl, Gavin Wright and seminarparticipants at Berkeley, Bologna, EIEF, Harvard, LMU, LSE, Northwestern Economic History Lunch, PSEMigration Seminar, Rochester, Stanford, UPF, Virtual Seminars in Economic History, Warwick, the Atlanta2019 EHA Annual Meetings, at the Galatina Summer Meetings, the Yale Politics and History Conference,and the 2020 Virtual World Congress of the Econometric Society for useful comments. We are grateful toEric Schickler and Kathryn Pearson for sharing with us data on signatures on discharge petitions, and toJames Gregory for sharing datasets on NAACP presence and CORE non-violent demonstrations. Silvia Farina,Ludovica Mosillo, Monia Tomasella, Francesca Bramucci, Pier Paolo Creanza, Martina Cuneo, Sarah O’Brein,Gisela Salim Peyer, and Arjun Shah provided excellent research assistance. All remaining errors are ours.

†Stanford University. Email: acald@stanford.edu‡Stanford University, Department of Political Science. Email: vfouka@stanford.edu§Harvard Business School and CEPR. Email: mtabellini@hbs.edu

1 Introduction

Racial inequality is a pervasive feature of US society, encompassing most of its domains – from

earnings to employment opportunities, from intergenerational mobility to incarceration rates.1

One of the potential causes of the racial gap and of its unwavering persistence is the lack of

political empowerment of Black Americans who, for a major part of American history, have

been denied even the most fundamental civil right in a democracy, namely the right to vote

(Aneja and Avenancio-Leon, 2019; Keyssar, 2009).

Black political oppression was particularly strong in the US South. Writing in 1944, Swedish

economist and Nobel Prize winner Gunnar Myrdal argued that migrating outside the region

represented the most effective strategy for Black Americans to achieve racial equality and

finally gain political rights (Myrdal, 1944). According to Myrdal, “[t]he average Northerner

does not understand the reality and the effects of such [Southern] discriminations”, and “[t]o

get publicity is of the highest strategic importance to [Blacks]”. Around this time, African

Americans started to move from the South to the North and West of the United States, hoping

to reach a “Promised Land” and leave behind them the system of disenfranchisement, violence,

and discrimination perpetuated by the infamous Jim Crow laws (Boustan, 2016; Wilkerson,

2011). Eventually, more than 4 million Black Americans migrated between 1940 and 1970 in

what is known as the Second Great Migration (henceforth, Great Migration).

The Great Migration temporally coincided with the development and eventual success of

the civil rights movement – a turning point in the history of race relations in the United States,

which culminated in the passage of the Civil and Voting Rights Acts of 1964 and 1965. Given

the resistance of southern politicians to extend the franchise to Black Americans, northern

legislators and grassroots organizations located in the North such as the National Association

for the Advancement of Colored People (NAACP) and the Congress of Racial Equality (CORE)

played a key role in the process of enfranchisement (Lawson, 1976). Was Myrdal right? Did

northward migration allow African Americans to gain political power?

In this paper, we study this question, analyzing the political effects of Black in-migration

to the US North and West between 1940 and 1970. Our analysis proceeds in two steps.

First, we examine how the Great Migration affected demand for civil rights and racial equality

among northern voters. We measure support for civil rights in several ways, but use as main

proxies the Democratic vote share in Congressional elections and the frequency of non-violent

pro-civil rights demonstrations organized by inter-racial grassroots organizations such as the

CORE. Even though the Democratic Party was openly segregationist and stubbornly defended

white supremacy in the South until the early 1960s (Kuziemko and Washington, 2018; Lawson,

1976), by the end of the 1930s in the North and West it had unambiguously become the party

defending Blacks’ interests and pushing for racial equality (Schickler, 2016; Wasow, 2020).2

Second, we analyze the effects of Black in-migration on the ideology and behavior of members

1See, among others, recent works by Bayer and Charles (2018) and Chetty et al. (2020). Previous, importantcontributions on this topic include Smith and Welch (1989) and Neal and Johnson (1996). See also the reviewin Altonji and Blank (1999).

2Below, we corroborate this idea, and provide evidence consistent with the existing literature (Feinstein andSchickler, 2008; Schickler, 2016). On party realignment during this historical period, see also recent work byCaughey et al. (2020).

1

of the House on race-related issues.

The political effects of the Great Migration are far from obvious. On the one hand, recent

work in economics has documented that the Great Migration had substantial, negative effects

on African Americans in the long run. Black in-migration to northern cities increased racial

residential segregation, as white residents fled urban areas for the suburbs (Boustan, 2010). In

turn, whites’ residential choices, coupled with changes in the allocation of local public goods

away from education and towards policing, drastically limited opportunities for economic and

social mobility of African Americans (Derenoncourt, 2018). Racial residential segregation and

lower economic opportunities may have been accompanied by whites’ political backlash and a

reduction in Blacks’ political efficacy.

On the other hand, the Great Migration might have promoted Blacks Americans’ political

empowerment for at least two reasons. First, around 1940, Blacks were de facto or de jure

prevented from voting in most southern states (Cascio and Washington, 2014), whereas no

restrictions to their political participation existed in the North. The inflow of Black voters

may have thus shifted (northern) politicians’ incentives to introduce civil rights legislation.

Second, Black arrivals may have moved the preferences of at least some white voters in a more

liberal direction. This might have happened either because the Great Migration increased

whites’ awareness of the conditions prevailing in the South, as envisioned by Myrdal (1944), or

because progressive parts of the Democratic coalition saw an opportunity to jointly promote

racial equality and economic goals by forming a cross-race alliance, as suggested by the political

science literature (Adams, 1966; Frymer and Grumbach, 2020; Schickler, 2016).

To study the political effects of the Great Migration we estimate stacked first difference

regressions, controlling for state time-varying (observable and unobservable) characteristics,

and allowing counties to be on differential trends depending on their initial Black share and

political conditions. We construct a version of the shift-share instrument (Boustan, 2010;

Card, 2001) that assigns Black outflows from each southern state to northern counties based

on pre-existing settlements of African Americans outside the South.

The shift-share instrument does not merely apportion more Black migrants to counties

with more African Americans in 1940, but rather, it combines two separate sources of varia-

tion. First, it exploits variation in the “mix” of Blacks born in different southern states and

living in different northern counties in 1940. Second, it leverages time-series variation in Black

emigration rates from different southern states for each decade between 1940 and 1970. Since

we always condition on the 1940 Black share of the population — interacted with period dum-

mies — the instrument only exploits variation in the composition of Black migrants across

southern states over time.

The validity of the instrument may be threatened if the characteristics of northern areas

– such as the fraction of the workforce in manufacturing or the urban share – that attracted

a different mix of southern born African Americans before 1940 had persistent, confounding

effects both on changes in racial attitudes and on migration patterns (Goldsmith-Pinkham

et al., 2020). We address this issue in two ways. First, we document that predicted Black

in-migration is not correlated with the pre-1940 change in political conditions across northern

counties. Second, we allow counties to be on differential trends by interacting year dummies

2

with several 1940 local characteristics, such as the Black, immigrant, and urban share of the

population, initial support for the Democratic Party, and the employment share in manufac-

turing. We also interact year dummies with the share of Blacks born in each southern state

to check that the variation behind the instrument is not disproportionately driven by specific

destination-origin combinations, which may also be spuriously correlated with the evolution of

political conditions in the North.

The instrument may also be invalid if shocks to northern counties both influenced the local

economic and political landscape and caused outmigration from southern states that already

had large enclaves in those counties before 1940 (Borusyak et al., 2018). To tackle this potential

concern, we first perform placebo checks to show that the instrument is uncorrelated with local

WWII spending (one of the key “pull factors” that attracted Blacks northward during the Great

Migration, e.g. Boustan, 2016) and New Deal relief programs. Second, we replicate the analysis

separately controlling for a measure of predicted labor demand, constructed by interacting the

1940 industry composition of northern counties with nation-level industry growth rates after

1940. Finally, as in Boustan (2010) and Derenoncourt (2018), we construct a modified version

of the instrument that exploits only variation in local push factors across southern counties to

predict Black outflows from the US South.3

Given the vast literature on “white flight” (Boustan, 2010; Shertzer and Walsh, 2019), we

verify that the Great Migration did not lead to white out-migration at the county level. We also

document that the composition of white residents did not change in response to Black inflows.

Notably, these results are not in contrast with previous work (Boustan, 2010; Derenoncourt,

2018). We provide evidence that county boundaries do not overlap with city-suburbs divides.

This, in turn, implies that changes in population triggered by Black inflows occurred within

(and not between) the jurisdictions that we consider in our analysis.

Turning to our main results, we find that Black in-migration had a strong and positive

impact on the Democratic vote share in Congressional elections. Our estimates imply that

one percentage point increase in the Black share raised the Democratic vote share by 1.6

percentage points, or 4% relative to the 1940 mean. This is a large effect: even under the

aggressive assumption that all Black migrants immediately voted for the Democratic Party

upon arrival, support for Democrats must have increased among northern residents because of

Black inflows. Complementing our electoral results, we find that Black arrivals increased both

the frequency of non-violent pro-civil rights demonstrations from CORE and the presence of

local NAACP chapters.

Consistent with the view that African Americans were quickly incorporated in the politi-

cal life of northern cities (Moon, 1948), we show that Black in-migration had a positive but

quantitatively small and imprecisely estimated impact on turnout. This indicates that Black

in-migration likely induced existing voters to switch away from the GOP. Since not all Black

residents were already voting for the Democratic Party in the early 1940s, some switchers were

probably African Americans. However, the magnitude of our estimates implies that some seg-

ments of the white electorate likely joined the Democratic voting bloc as well. Using a subset

3By exploiting southern county specific shocks, this strategy also assuages potential concerns over serialcorrelation in migration flows from the same location to the same destination (Jaeger et al., 2018).

3

of the data on pro-civil rights demonstrations, which reports the race of participants, we find

that the propensity to join pro-civil rights demonstrations increased not only among Black but

also among white individuals. This is consistent with our previous interpretation that some

whites became more supportive of racial equality because of Black arrivals.

To understand which segments of the white electorate became more supportive of civil

rights, we explore heterogeneity patterns in our results. Focusing on pro-civil rights demon-

strations, we exploit variation in county 1940 composition and historical characteristics.4 First,

we consider the possibility that the Great Migration raised support for civil rights among so-

cially progressive whites by increasing the salience of the “race problem” and activating their

latent demand for racial equality (Allport, 1954; Myrdal, 1944). In line with this view, we

show that pro-civil rights demonstrations were concentrated in counties with a lower history

of racial discrimination, and where the white population was younger and less likely to come

from the US South.

Second, we document that pro-civil rights demonstrations were more likely to occur in areas

with a higher share of whites employed in manufacturing and in the unskilled sector, where the

presence of the CIO was stronger, and where elections were more competitive. These places may

have offered fertile grounds for the formation of a liberal cross-race coalition along political and

economic lines, as discussed extensively in Schickler (2016). Labor unions may have supported

a cross-race coalition only, or especially, when labor markets were tight (Bailer, 1944). Indeed,

our results are driven by counties where labor demand, predicted using a Bartik-style approach,

was stronger.

We corroborate our heterogeneity analysis by providing suggestive evidence from historical

survey data from the American National Election Studies (ANES) and from Gallup. Estimating

state-level cross-sectional regressions, we find that, in the years preceding the Civil Rights Act

of 1964, white respondents living in states that received more Black Americans between 1940

and 1960 held significantly more favorable views on race relations, considered racial equality as

one of the most fundamental issues for the country, and were more likely both to identify with

and to vote for the Democratic Party.5 These results are driven by liberal and Democratic

respondents, and by members of labor unions.

Our findings may seem at odds with the literature on white flight and the detrimental

consequences that the latter had on Black migrants and their offspring in the long run (Boustan,

2010; Derenoncourt, 2018).6 However, Black political empowerment and white flight are not

necessarily in contrast with each other. For one, there is extensive evidence that the Great

Migration did economically benefit Black migrants (Baran et al., 2020; Boustan, 2016; Collins

and Wanamaker, 2014). In addition, white voters may have supported civil rights, while at

the same time moving from central cities to the suburbs (within the same county). From

the lens of a Tiebout (1956) framework, white voters may have expressed their preferences

regarding residential segregation and local public spending (schooling, in particular) with their

4Since data on respondents’ race is available only for a limited subset of events, we conduct this heterogeneityanalysis for all demonstrations, and not just on those involving white (and Black) participants.

5We are unable to conduct this analysis at the county level, as the ANES reports county identifiers only from1978 onwards.

6The Great Migration also increased racial disparities in incarceration rates (Eriksson, 2019; Muller, 2012),and worsened public finances in northern cities (Tabellini, 2018). See Collins (2020) for a thorough review.

4

feet, while instead using the ballot box to express their more general ideological preferences

about political racial equality. Supporting this conjecture, pro-civil rights demonstrations were

more likely to take place in counties where 1940 residential segregation was higher. That is,

when inter-group contact in the housing market was lower, whites were more supportive of

civil rights. Moreover, Black in-migration increased the number of school districts in counties

with higher 1940 levels of residential segregation. These patterns indicate that whites in more

segregated counties simultaneously showed higher support of civil rights and founded new local

jurisdictions to further exclude Blacks.

In the second part of the paper, we turn to the effects of the Great Migration on the ideology

and behavior of legislators representing non-southern congressional districts (CDs). Similar to

Autor et al. (2020), we construct a cross-walk that matches counties to CDs, and then develop

a procedure that assigns CD boundaries, which changed over time due to redistricting, to the

geography of a baseline, the 78th, Congress.7 We measure legislators’ ideology using the civil

rights scores constructed by Bateman et al. (2017), which are based on a legislator’s past voting

behavior on civil rights bills, and take more negative (resp. positive) values for more liberal

(resp. conservative) ideology. Mirroring our results in the electorate, over time, CDs that

received more African Americans were represented by legislators with a more liberal ideology

on racial issues over time. To more directly capture legislators’ behavior, we also show that

Black in-migration had a strong, positive effect on the probability of signing discharge petitions

aimed at promoting civil rights bills (Pearson and Schickler, 2009; Schickler, 2016).

Exploring the heterogeneity behind these average effects, we obtain two sets of results.

First, comparing legislators in CDs that remained Democratic throughout the period to leg-

islators representing CDs that always remained Republican, the ideology of the latter moved

significantly to the right on racial issues relative to that of the former. Second, as in Autor et al.

(2020) and in Tabellini (2020), we classify legislators in four categories – liberal Democrats,

moderate Democrats, moderate Republicans, and conservative Republicans – and show that

Black in-migration increased the probability of electing a liberal Democrat in the 1940s, but

a conservative Republican in the 1950s. Since quantitatively the first effect prevailed over the

second one, on average, legislators’ ideology moved to the left. However, when inspecting these

dynamics more carefully, polarization becomes evident.

This paper contributes to various strands of literature. First, it is related to the literature

on the civil rights movement. Several papers have studied the consequences of the Civil Rights

and the Voting Rights Acts (Aneja and Avenancio-Leon, 2019; Bernini et al., 2018; Cascio

and Washington, 2014; Cascio et al., 2010; Reber, 2011), while many others, building on the

seminal contribution by Carmines and Stimson (1989), have investigated the causes of the

southern “dealignment” (Besley et al., 2010; Kousser, 2010; Kuziemko and Washington, 2018;

Trende, 2012; Wright, 2013). We contribute to this literature by examining one of the causes of

the civil rights movement, and showing that the Great Migration likely played a key role in the

success of the latter. Our findings are also consistent with and complement Schickler (2016)

and Grant (2020) who, respectively, argue that the incorporation of African Americans into

the Democratic coalition after the New Deal and the rising pivotal role of African American

7The appendix describes this procedure in detail and performs a number of robustness checks.

5

voters at the national level due to the Great Migration were important mechanisms behind the

racial realignment of American politics.

Second, we complement the vast and growing literature on the political effects of migration

and the broader literature on inter-group relations. Several papers find that immigration and

a larger size of the minority group can lead to backlash among natives or majority members

(Arzheimer, 2009; Dustmann et al., 2019; Enos, 2016; Tabellini, 2020). We instead show that,

under certain conditions, inter-group contact can favor the formation of cross-race social or

political coalitions, raising demand for racial equality also among members of the majority

group.8 Several factors can explain the difference between our findings and those in the existing

literature. First, Black in-migration might have increased whites’ awareness of the conditions

prevailing in the South (Myrdal, 1944). Second, the civil rights legislation was, by and large,

about the South, and northern white voters would have been only indirectly – if at all –

affected at least before 1965. Third, labor unions had political and economic incentives to

incorporate African Americans in their rank and files (Adams, 1966; Bailer, 1944; Schickler,

2016), forging a shared working class identity and pursuing common goals, conditions that

contributed to positive inter-group contact (Allport, 1954). Finally, our average effects mask

substantial heterogeneity. On the voters’ side, support for civil rights increased more in more

segregated counties. On the side of legislators, the Great Migration substantially increased

polarization along party lines.

Our results also speak to the literature on the relationship between voters’ demand and

politicians’ behavior (Caughey and Warshaw, 2018; Jones and Walsh, 2018; Kroth et al., 2016;

Lott and Kenny, 1999; Mian et al., 2010; Miller, 2008). Closest to our paper, Cascio and

Washington (2014) have documented that the Voting Rights Act (VRA) shifted the distribution

of local spending across southern counties towards Blacks, once the latter became eligible to

vote. We expand on their findings by focusing on the US North rather than the South, and by

analyzing one of the potential causes, rather than consequences, of the VRA – i.e., the response

of northern politicians to the change in the characteristics, and thus in the demands, of their

constituency due to Black in-migration.

Finally, we complement the literature on the Great Migration, recently reviewed in Collins

(2020). Although several papers in economics have studied its effects on the residential decision

of whites, intergenerational mobility, immigrant assimilation, and public finance (Boustan,

2010; Shertzer and Walsh, 2019; Derenoncourt, 2018; Fouka et al., 2018; Tabellini, 2018), little

evidence exists on its political effects. Our paper seeks to fill this gap, focusing on the role that

the Great Migration played in the development and the success of the civil rights movement.

The paper proceeds as follows. Section 2 describes the historical background. Section 3

presents the data, and Section 4 lays out the empirical strategy. Section 5 studies the effects

of the Great Migration on demand for civil rights legislation, and verifies that Black inflows

were not associated with white flight at the county level. Section 6 investigates how Congress

members responded to Black in-migration. Section 7 concludes.

8Our findings are consistent with those in Lowe (2017), Rao (2019), and Steinmayr (2020) from India andAustria respectively. We complement them by providing evidence from the US and in an instance where groupboundaries are defined by race rather than by caste, income, or refugee status.

6

2 Historical Background

2.1 The Great Migration

Between 1940 and 1970, more than 4 million African Americans left the US South for northern

and western destinations. This unprecedented migration episode is usually referred to as the

(Second) Great Migration. From 1915 to 1930, the First Great Migration brought to the

North 1.5 million Blacks. However, the Second Great Migration – from now onwards the

Great Migration – was substantially larger in magnitude and had more profound implications

for American politics and race relations (Boustan, 2016). Most Black migrants moved to

urban centers in the Northeast and mid-West, but the Great Migration was a geographically

widespread phenomenon, which affected also the West and less urbanized areas outside the

South (Figure 1).9

Black migrants were pulled to the North and West by economic opportunities and pushed

out of the South by racial oppression, political disenfranchisement, and poor working conditions

(Boustan, 2016). On the one hand, the outbreak of WWII increased demand for labor in

northern and western factories, raising the potential gains from migration. Even after the

WWII-related labor demand shock was over, higher expectations of upward social and economic

mobility kept attracting African Americans to the North at least until the late 1960s. On the

other hand, widespread violence and disenfranchisement, together with a separate and unequal

school system, provided strong incentives for Blacks to leave the South (Feigenbaum et al.,

2020; Margo, 1991). Moreover, the mechanization of agricultural harvest in the 1940s and

1950s reduced demand for labor in the already depressed southern agricultural sector, further

increasing the pool of prospective migrants (Grove and Heinicke, 2003; Whatley, 1985).

Out-migration from the South was strongest during the 1940s, with a Black emigration

rate of almost 15%, but remained high until the late 1960s (Figure A.1). As a consequence of

this migration episode, during which the US South lost 40% of its 1940 Black population, the

racial profile of the United States changed dramatically. While only 25% of African Americans

were living outside the South in 1940, this figure had increased to more than 50% by 1970.

On average, the Black share of the population in northern and western cities moved from less

than 4% to more than 15% in just three decades. These numbers were an order of magnitude

higher for main hubs like Chicago, Detroit, or St. Louis, where the Black share moved from 8,

9, and 11% to 32, 43, and 41% respectively (Gibson and Jung, 2005).10

2.2 Black Migrants and Northern Politics

The demographic change induced by the Great Migration had the potential to alter the political

equilibrium, especially in industrial and urban centers. In the US South, Blacks were de facto

and de jure disenfranchised through the use of literacy tests, poll taxes, and grandfather clauses

(Cascio and Washington, 2014; Lawson, 1976). On the contrary, they could, and in fact did,

9When defining the US South, we follow the Census classification but, as in Boustan (2010), we excludeMaryland and Delaware – two states that received net black inflows during the Second Great Migration. SeeTable A.1 for the list of southern states considered in the paper.

10In rural counties, the Black share remained substantially lower and rarely exceeded 2 or 3%.

7

vote in the North (Moon, 1948). The literature on social movements has documented that

the enfranchisement of Black migrants increased both the organizational capacity of the civil

rights movement and pressures on local politicians (McAdam, 1982). During the First Great

Migration, both Democrats and Republicans had tried to include African Americans in their

voting bloc. However, since the New Deal, the Democratic Party had emerged as the party

better equipped to address Blacks’ demands outside the US South (Caughey et al., 2020;

Schickler, 2016).

Figure A.2 plots the share of northern Democrats (blue bars) and Republicans (red bars)

voting in favor of civil rights bills between Congresses 78 and 88 (see Table A.2 for the detailed

list of bills). Both in the 1940s and in the 1950s, Democrats in the North were more likely to

support civil rights bills.11 Using data from Pearson and Schickler (2009), Figure A.4 confirms

these patterns by focusing on signatures on pro-civil rights discharge petitions – another, more

direct, measure of legislators’ commitment to racial equality (Schickler, 2016).12 Non-southern

Democratic Congress members were at least 30 percentage points more likely than their Re-

publican counterparts to sign a discharge petition to promote civil rights legislation between

Congress 78 and Congress 82. The gap rose to more than 50 percentage points in the following

decade (Table A.3).

Recognizing the different party position on racial issues, Blacks in the North were signifi-

cantly more likely to support the Democratic Party. Existing evidence indicates that at least

70% of registered Black voters outside the South were voting Democratic already in 1936 –

a share that gradually increased over time (Bositis, 2012). Taking advantage of its initial

position, the Democratic Party successfully incorporated Black migrants in its voting bloc.

Democrats also benefited from the behavior of labor unions – the Congress of Industrial Or-

ganizations (CIO) in particular – that, since the late 1930s, started to actively incorporate

African Americans in their ranks.13

Abundant anecdotal evidence exists that labor unions openly endorsed civil rights and

backed African Americans in their fight for racial equality (Adams, 1966; Bailer, 1944). For

instance, J. Brophy, one of the CIO leaders, declared in 1944 that “behind every lynching is the

figure of the labor exploiter...who would deny labor its fundamental rights”. On a similar vein,

in 1942 Walter Reuther, a highly influential figure in the United Automobile Workers (UAW),

declared that “...[racial discrimination] must be put on top of the list with union security and

other major union demands” (Zieger, 2000). In line with these statements, evidence from the

Congressional Quarterly Almanac shows that, for the 42 cases in which the NAACP took a clear

position on a proposed piece of legislation between 1946 and 1955, the CIO openly took the

very same position in 38 cases, and never took a position conflicting with that of the NAACP

(Schickler, 2016). As a result, a class-based coalition, pushing for both racial and economic

liberalism, emerged. This gave additional leverage to Black activists and organizations such as

the NAACP and the CORE to exert pressure on northern Democrats to pursue the civil rights

11Figure A.3 documents that the pattern is reversed once the US South is included.12At a time when southern Democrats could block any proposed civil rights-related bill even before it reached

the floor of the House, discharge petitions were filed by northern legislators to circumvent congressional com-mittees, and move bills to the floor for a vote (Beth et al., 2003). For more details see Section 3.

13Using survey data from Gallup, Farber et al. (2018) document that, while non-southern whites were signif-icantly more likely than Blacks to be union members in 1940, this pattern had been reversed by 1960.

8

agenda.

3 Data

Our study combines data from several sources. In this section, we review the variables consid-

ered in our analysis, and we refer the reader to Appendix D for a more detailed description of the

surveys used in the paper. To analyze legislators’ behavior, we construct a time-invariant cross-

walk from counties to CDs, fixing CD boundaries to the baseline Congress of 1944 (Congress

78). Appendix B describes this procedure in detail.

Black in-migration and demographic variables. Data on Black and total population

as well as on other demographic variables for non-southern counties come from the County

Databooks, from Haines et al. (2010), and from the 1940 full count Census of Population

(Ruggles et al., 2015). We also collected data on Black migration rates from Gardner and

Cohen (1992) and Bowles et al. (1990) for 1940-1950 and for 1950 to 1970 respectively.

Electoral outcomes. Since the New Deal, Democrats had become the pro-Black party

outside the US South (Caughey et al., 2020; Moon, 1948). Such racial realignment was more

likely to emerge in Congressional than in nation-wide Presidential elections (Schickler, 2016).

Motivated by these observations as well as by the evidence presented in Section 2.2, we focus

on the Democratic vote share and on turnout (defined as the share of votes cast in the election

over the total number of eligible voters in the county) in Congressional elections. Data are

taken from Clubb et al. (1990). Since Census data are available at the decennial level, and

because Congressional elections are held every two years, we focus on electoral returns for exact

Census years from 1940 to 1970.

Local support for civil rights. We obtain measures of local support for the civil rights

movement from two sources. First, we use the dataset assembled by Gregory and Hermida

(2019) combining a variety of sources that includes the number of non-violent demonstrations

organized between 1942 and 1970 by the CORE – an inter-racial group of students from the

University of Chicago that coordinated sit-ins and similar forms of civil disobedience mainly

across northern cities to protest against segregation in the South. Second, we collect data on

the presence of NAACP chapters from Gregory and Estrada (2019). In both cases, we match

the geographic coordinates of an event or of a NAACP chapter to the centroid of each county

in our sample.14

Whites’ attitudes. We collect data on whites’ racial attitudes and stance on civil rights

from two, nationally representative surveys: the ANES and Gallup. Both are cross-sectional

datasets that report individuals’ socioeconomic and demographic characteristics as well as

their political ideology. Starting from the mid to late 1950s, both surveys began to elicit

respondents’ views on racial equality and their support for civil rights. Even though neither

dataset includes a county or city identifier, they both report respondents’ state of residence,

allowing us to correlate the change in the Black share at the state level with attitudes of

white respondents interviewed between the late 1950s and the mid-1960s (when the CRA was

14Data on NAACP chapters are available only for the early 1940s and the early 1960s.

9

passed).15

Legislators’ ideology. We measure the ideology of northern legislators on civil rights

by using the scores constructed by Bateman et al. (2017). As for the commonly used DW

Nominate scores (Poole and Rosenthal, 1985), legislators are assigned a score that is a function

of their past voting behavior and takes more negative (resp. positive) values for more liberal

(resp. conservative) positions. We rely on the Bateman et al. (2017) scores for two reasons.

First, they are calculated by restricting attention solely to civil rights bills, as classified by

Katznelson and Lapinski (2006). Second, they allow the policy content to be Congress specific

and to vary over time.16

Signatures on discharge petitions. Exploiting the seniority system prevailing at the

time, southern Democrats were able to block most proposed civil rights-related bills even before

they reached the floor of the House (Schickler, 2016). To overcome this obstacle, northern

legislators could rely on discharge petitions to move a bill to the floor, provided that a petition

collected at least 218 signatures (Beth et al., 2003). Using data from Pearson and Schickler

(2009), we measure a legislator’s involvement with (and support for) civil rights with signatures

on discharge petitions concerning racial issues.

Table 1 presents summary statistics for the main variables considered in our analysis, re-

porting 1940 levels in Panel A and their (decadal) changes in Panel B. The Black share in the

average county was around 3.5% in 1940, and increased to almost 9% in 1970 (not shown).17

These average values, however, mask substantial heterogeneity. Figure A.5 plots the 1940 Black

share for the counties in our sample, and shows that, in 1940, Blacks living outside the South

were concentrated in the urban centers of the Northeast and the Midwest, in border states like

Missouri and Kansas, and in southern California and some areas of Arizona and New Mexico.

For example, in Cook County (IL), the Black share in 1940 was already as high as 8%, and

rose to 21.5% by 1970. Similarly, the Black share in Philadelphia County (PA) increased from

more than 12% in 1940 to almost 35% in 1970, whereas that in Alameda County (CA) rose

from 2 to 15% during the same period (Figure A.6).

Turning to our main outcomes, the 1940 Democratic vote share in Congressional elections

was on average 45.7%. Focusing on the 78th Congress, our baseline Congress year, civil rights

scores were on average negative (-0.87), indicating that northern legislators were relatively

liberal on racial issues already by 1940. The average decadal change in ideology scores was very

close to zero, even though this masks important differences both between parties and between

Congress periods (Bateman et al., 2017; Schickler, 2016). Finally, signatures on discharge

petitions were significantly more common in the 78th- 82nd than in the 83rd - 88th Congress

period (Tables A.4 and A.5), and their subjects changed markedly over time. While the poll

15The restricted use version of the ANES includes county of residence of respondents from 1978 onwards.More details about the survey questions used in our analysis are provided in Appendix D.

16Bateman et al. (2017) develop two main versions of the score – one that assumes that the ideal points oflegislators remain constant over time, and one that instead does not make such assumption. As shown below,our results are robust to using either of the two versions.

17We drop non-southern counties that had no African American population in 1940, for which the instrumentfor Black in-migration (described below) cannot be constructed. Results are robust to restricting attention tocounties with a 1940 Black population above different thresholds or to including also counties with no Blackresidents at baseline.

10

tax and FECP legislation were the most common topics during the 1940s, 5 of the 8 discharge

petitions filed between the 83rd and the 88th Congress concerned the CRA.

4 Empirical Strategy

4.1 Estimating Equation

Our empirical analysis is divided in two parts. First, we estimate the effects of the Great

Migration on demand for civil rights legislation; second, we analyze the response of northern

legislators to changes in the composition and preferences of their electorate. To be clear: we

do not attempt to isolate the impact of changes in voters’ demand, due to Black inflows,

on legislators’ behavior. In fact, both parties likely re-optimized their platforms strategically

because of the Great Migration, in turn influencing the actions of voters – both Blacks and

whites. Our goal is instead to estimate the “reduced form” effect of Black in-migration on

voters’ demand and politicians’ supply without taking a stance on how the two influenced each

other.

Starting from the demand side and stacking the data for the three decades between 1940

and 1970, we estimate

∆ycτ = δsτ + β∆Blcτ + γXcτ + ucτ (1)

where ∆ycτ is the change in the outcome of interest in county c during decade τ . When

focusing on electoral outcomes, ycτ refers to the Democratic vote share and turnout in Con-

gressional elections. When considering grassroots activism, ycτ is the probability of pro-civil

rights demonstrations organized by the CORE and the presence of local NAACP chapters. In

order to identify the effects for the average county, we weigh regressions by 1940 county popu-

lation, but, as shown in Appendix C, results are robust to estimating unweighted regressions.

Standard errors are clustered at the county level.

The key regressor of interest, ∆Blcτ , is the change in the Black share in county c during

period τ . δsτ includes interactions between period and state dummies, and Xcτ is a vector of

interactions between period dummies and 1940 county characteristics. Our preferred specifica-

tion includes the 1940 Black share and a dummy equal to one if the Democratic vote share was

higher than the Republican vote share in the 1940 Congressional elections, but in Appendix C

we add more interactions to probe the robustness of our results. Since equation (1) is taken

in stacked first differences and always controls for interactions between period and state dum-

mies, the coefficient of interest, β, is estimated from changes in the Black share within the

same county over time, as compared to other counties in the same state in a given period.

Turning to the ideology and the behavior of legislators, we estimate a version of equation

(1), with a few differences. First, the unit of analysis, c, is no longer the county but, instead,

the CD.18 Second, when considering ideology scores, we restrict attention to two – rather than

three – periods. This allows us to end our analysis with the Congress that passed the CRA

(Congress 88). Third, when focusing on signatures on discharge petitions, we are forced to

18As noted above, we rely on the time-invariant unit of analysis constructed in Appendix B to deal withredistricting. Regressions are weighed by CD population, and standard errors are clustered at the CD level.

11

estimate equation (1) only for the 78-82 Congress period, when a sufficient number of petitions

were filed both at the beginning and at the end of the decade.

4.2 Instrument for Changes in Black Population

The key empirical challenge for our analysis is that Black migrants might have sorted in places

that were already undergoing economic and political changes. To overcome these and similar

concerns, we predict Black inflows in northern area c during decade τ using a version of the

shift-share instrument commonly adopted in the immigration literature (Boustan, 2010; Card,

2001). The instrument predicts the change in the Black population in county c during decade

τ by interacting the share of Blacks born in southern state j and living in northern county c

in 1940 (relative to all Blacks born in state j living outside that state in 1940), shjc, with the

number of Blacks who left state j during period τ , Bljτ :

Zcτ =∑

j∈SouthshjcBljτ (2)

Since we are interested in the effects of changes in the Black share, we scale Zcτ by 1940 county

population.

As discussed in Boustan (2010) among others, Black settlements in the North were highly

persistent over time. At the turn of the twentieth century, as African Americans started to move

northwards, migration patterns were influenced by the newly constructed railroad network. For

instance, the presence of the Illinois Central, which was connecting several Mississippi counties

to Chicago and a number of southern railroads to northern hubs in Missouri and Illinois,

explains why Black migrants from Mississippi were disproportionately concentrated in Chicago

or St. Louis (Grossman, 1991). The stability of Black enclaves was further reinforced by the

process of chain migration during the First Great Migration (Collins and Wanamaker, 2015).

Figure A.7 plots the share of Blacks born in Alabama, Mississippi, and Texas living in selected

northern counties in 1940, documenting the wide variation in settlement patterns across both

destination and origin areas.

4.2.1 Identifying Assumptions and Instrument Validity

Several recent papers have discussed the potential threats to the validity of shift-share designs

(Adao et al., 2019; Borusyak et al., 2018; Goldsmith-Pinkham et al., 2020; Jaeger et al., 2018).

In our setting, one threat to identification is that the characteristics of counties that pulled

Black migrants from specific states before 1940 may also be correlated both with post-1940

(Black) migration patterns from the South and with changes in support for civil rights in

northern counties (Goldsmith-Pinkham et al., 2020). We deal with this concern by performing

two sets of robustness checks, which are described in detail in Appendix C. First, we show that

pre-period changes in the outcomes of interest are not correlated with post-1940 changes in

Black in-migration predicted by the instrument. Second, we augment our baseline specifica-

tion by interacting year dummies with several 1940 county characteristics, such as the Black

share, support for the Democratic Party, the urban share of the population, and the share of

12

employment in manufacturing.

Controlling for the interaction between the 1940 Black share and year dummies implies

that the instrument only exploits variation in the (southern state) composition of African

Americans’ enclaves across counties, holding constant the size of their Black populations. We

also replicate our analysis by interacting year dummies with the share of Blacks born in each

southern state living in northern and western counties in 1940, i.e. shjc in equation (2). This

exercise reduces the concern that our estimates may be sensitive to variation coming from the

initial shares of Blacks born in specific southern states and concentrated in selected northern

counties (Goldsmith-Pinkham et al., 2020).

A second threat to the validity of the instrument is that it may be spuriously correlated

with specific shocks hitting northern counties that both affected local economic or political

conditions and influenced emigration patterns across southern states over time (Borusyak et al.,

2018). We address this concern in different ways. First, we document that the instrument is

uncorrelated with either WWII spending or the generosity of New Deal relief programs across

counties. Second, similar to Sequeira et al. (2020) and Tabellini (2020), we replicate the

analysis by separately controlling for a measure of predicted labor demand, constructed by

interacting the 1940 industrial county composition with the national growth rate of different

industries between 1940 and 1970. Third, as in Boustan (2010) and Derenoncourt (2018),

we replace actual outmigration from the South with that estimated by exploiting only initial

conditions across southern counties. This strategy also deals with the potential concern of

serial correlation in migration flows from the same location to the same destination (Jaeger

et al., 2018).

5 Demand for Civil Rights Legislation

This section studies the effects of the Great Migration on demand for civil rights. Section 5.1

documents that Black inflows increased the Democratic vote share in Congressional elections

and encouraged grassroots activism in support of racial equality. Section 5.2 verifies that the

Great Migration was not associated either with white flight between counties or with changes

in the composition of white residents, and summarizes additional robustness checks, which are

described in detail in Appendix C. Section 5.3 examines our results in more detail, considering

also the potential change in whites’ attitudes and political preferences.

5.1 Main Results

5.1.1 Congressional Elections

We start by studying the effects of the Great Migration on the Democratic vote share in Con-

gressional elections, which we interpret as a proxy for voters’ demand for civil rights legislation.

Panel A of Table 2 estimates equation (1) with OLS in columns 1 to 3, and with 2SLS from

column 4 onwards. Column 1 only includes state by year fixed effects, while columns 2 and 3

add interactions between year dummies and, respectively, the 1940 Black share and an indica-

tor for Democratic incumbency in 1940. In all cases, the point estimate on the change in the

13

Black share is positive and statistically significant.

Turning to 2SLS, Panel C of Table 2 shows that the instrument is strongly correlated with

the corresponding endogenous regressor, and the KP F-stat for weak instruments is always

above conventional levels. In our preferred specification – which includes interactions between

year dummies and: i) state dummies; ii) the 1940 Black share; and iii) an indicator for

Democratic incumbency in 1940 – the first stage coefficient implies that one percentage point

increase in the predicted Black share raises the actual Black share by 1.15 percentage points

(column 6).

2SLS estimates confirm the OLS results, but are larger in magnitude, especially for our

preferred specification (column 6), and when estimating long difference regressions (column

7). Our estimates are quantitatively large: according to our preferred specification (column

6), one percentage point increase in the Black share raised the Democratic vote share by

1.65 percentage points, or 4% relative to the 1940 mean. For large recipient counties such

as Cook county (IL) or Wayne county (MI), where the Black share increased by more than

15 percentage points between 1940 and 1970, Black in-migration likely altered the political

landscape dramatically.

The difference between OLS and 2SLS estimates indicates that Black migrants selected

areas where support for the Republican Party was rising faster. This might have happened

because these counties were experiencing faster income growth.19 Another possibility, not in

contrast with the previous one, is that the IV identifies a local average treatment effect (LATE)

for counties that received more Black migrants because of family networks and not because

of economic conditions. If Black Americans moving to a specific location due to the presence

of networks were more politically engaged relative to “economic migrants”, this could explain

why OLS coefficients are smaller than 2SLS ones.20

Panel B of Table 2 estimates the impact of Black in-migration on turnout. Our preferred

2SLS specification (column 6) indicates that Black arrivals had a positive but small and im-

precisely estimated effect on turnout in Congressional elections. As for the Democratic vote

share, OLS coefficients are smaller – in fact, even negative in this case – than 2SLS ones. The

quantitatively small effect on turnout is in line with existing evidence that Black migrants

were quickly incorporated in the political life of northern and western counties (Moon, 1948;

Schickler, 2016).

Table A.6 (columns 1 to 3) replicates the analysis separately for each of the three decades,

focusing on the preferred specification (Table 2, column 6). The Great Migration had a very

strong effect on the Democratic vote share in the 1940-1950 (column 1) and, though to a lesser

extent, in the 1960-1970 (column 3) decades. Conversely, the point estimate becomes smaller in

magnitude and not statistically significant for the 1950s (column 2). Turnout follows a similar

pattern, with a higher point estimate in the 1940s and in the 1960s, but results are imprecise

and never statistically significant.

19Corroborating this idea, in our sample there is a negative and statistically significant relationship betweenthe change in the Democratic vote share and a number of proxies for economic growth, such as populationgrowth, population density, and industrial expansion.

20Intuitively, one would expect political participation to be increasing in the size of one’s own network. Fur-thermore, migrants attracted solely by economic forces may remain more isolated from local (Black) communitiesand their political demands.

14

One interpretation of these findings is that the 1940s saw the dawn of the civil rights move-

ment, which was partly spurred by the Double V Campaign organized by African American

activists during WWII (Qian and Tabellini, 2020). The 1960s culminated with the passage of

the CRA and of the VRA and, even though in the later period whites’ backlash erupted in

many northern and western cities (Collins and Margo, 2007; Reny and Newman, 2018), this

may have been partly offset by greater engagement among Blacks. The lack of significance

and the smaller magnitude of the coefficient for the 1950s is consistent with the idea that the

economic downturn at least temporarily halted the progress of race relations, and cooled off

whites’ support for racial equality (Sugrue, 2014).

As discussed in Schickler (2016), support for racial equality was stronger within the local

fringes of the Democratic Party. Moreover, when it came to national politics, African Amer-

icans remained more skeptical about the commitment of Democrats to the civil rights cause.

Replicating the preferred specification of Table 2 for Presidential elections, column 4 of Table

A.6 confirms this idea. For the Democratic vote share (Panel A), the coefficient on the change

in the Black share remains statistically significant and positive, but is one third smaller than

for Congressional elections. The point estimate on turnout (Panel B) is somewhat larger, but

not statistically significant.

The positive effect of the Great Migration on the Democratic vote share likely reflects a

combination of i) migrants’ direct political engagement, and ii) existing residents’ changes in

voting behavior and preferences. The fact that Black in-migration increased the Democratic

vote share by more than one for one (Table 2, column 6) points to the importance of changes in

the voting behavior of northern residents. As noted in Section 2.2, the best available estimates

indicate that around 70% of northern Black registered voters supported the Democratic Party

in 1940 (Bositis, 2012). Moreover, turnout among African Americans was lower than average

(around 70% in 1940). This suggests that the Great Migration both induced a fraction of Black

GOP supporters to switch to the Democratic Party and increased the political engagement of

northern Blacks who were previously not voting, drawing them into the Democratic voting

bloc. The co-movement of the point estimates for the Democratic vote share and turnout (see

columns 1 to 3 in Table A.6) further highlights the importance of Blacks’ political participation

for the rise of the civil rights movement in the North.

According to the estimates reported in Table 2 (column 6), we can reject the null hypothesis

that the coefficient on changes in the Black share is equal to 1 at the 5% level. This suggests

that, besides the mobilization of Black northerners and the direct behavior of Black migrants,

the Great Migration induced at least some white residents to change their voting behavior too.

We return to these points in Section 5.3 below, when exploring the heterogeneity of our results.

5.1.2 Pro-Civil Rights Demonstrations and NAACP Chapters

In Table 3, we focus on the frequency of non-violent demonstrations organized by the CORE in

support of civil rights. The structure of the table mirrors that of Table 2, reporting OLS and

2SLS estimates in columns 1 to 3 and 4 to 7 respectively, and presenting first stage coefficients

in Panel B. For brevity, we focus on our 2SLS preferred specification (column 6).

Black in-migration had a strong, positive effect on the probability of CORE demonstrations.

15

One percentage point increase in the Black share led to a 4.7 percentage point increase in the

likelihood of protests. The CORE was created in 1942, and the frequency of events in our

sample of counties between 1942 and 1944 (included) was 0.09. Our estimates thus imply that

one percentage point increase in the Black share raised CORE demonstrations by more than

50% relative to their pre-1945 values. Another way to gauge the magnitude of these estimates

is to consider that the average change in the probability of CORE-sponsored demonstrations

in our sample is 0.143. Thus, one percentage point increase in the Black share can explain

almost one third of the change in pro-civil rights demonstrations across non-southern counties

between 1940 and 1970.

Using detailed information on the cause and the target of the protest available in the CORE

dataset, we classified the pro-civil rights demonstrations in different categories. Figure A.8

plots the number of events in each of the top four categories – discrimination in access to

goods and services (e.g. restaurants or hotels), school segregation, residential segregation, and

police brutality – as a share of all demonstrations in our sample.21 Each bar in the figure also

indicates the share of events within each category that concerned national (dotted bar area)

and local (Black bar area) issues. Almost two thirds of the events concerned local issues – such

as boycotting a local taxi company for its discriminating hiring process in Seattle or protesting

against a white-only barbershop in Chicago – but there existed substantial heterogeneity across

categories. For instance, while more than 80% of the events organized to demand a reduction

in residential discrimination were focused on local issues, almost 40% of demonstrations in the

“access to goods” category were conducted on a more national platform.

Relying on this classification, Figure A.9 replicates the analysis of Table 3 (column 6) for

each category separately. The first four dots from the left report 2SLS coefficients when the

dependent variable is the change in the probability of demonstrations for each of the causes

reported in Figure A.8. The remaining two crosses on the right report results for the change

in the probability of local and national demonstrations respectively.22 Even though the point

estimate is always positive, it is statistically significant and quantitatively larger for protests

against discrimination in access to goods and services and against school segregation (first

and second dots from the left). The coefficient is also larger and more precisely estimated for

demonstrations with local, rather than national, targets – something to be expected, since the

CORE was operating through local branches.

In Table A.8, we turn to the 1940-1960 change in the probability that a county had a

NAACP chapter in place, presenting OLS and 2SLS estimates in columns 1-2 and 3-4 respec-

tively, and reporting first stage coefficients in Panel B.23 In the full sample of counties, Black

inflows did not have any statistically significant effect on the local presence of NAACP chapters

(column 3). However, the impact of Black in-migration becomes positive, statistically signif-

icant, and quantitatively relevant for counties that did not have a chapter in 1940 (column

4). The fact that we do not find any effect for counties that already had a chapter in place in

1940 is not surprising. In these places, Black inflows likely increased the number of members

21Since events were classified according to either the cause or the target of the demonstration, the categoriesin Figure A.8 do not add to one.

22Table A.7 presents the corresponding 2SLS coefficients.23As noted in Section 3, data on NAACP chapters are only available for 1940 (or earlier) and 1960.

16

of NAACP chapters – something that we are not able to measure in our data. Instead, in

counties where the NAACP was not present at baseline, Black in-migration probably created

a critical mass of activists that justified the opening of new local chapters.

5.2 Robustness Checks

5.2.1 Addressing White Flight

A potential concern with results presented thus far is that Black arrivals induced white residents

to move to another county. Under this scenario, our estimates would conflate the causal effect

of Black in-migration with the compositional change in the county electorate driven by white

flight. This issue may be particularly poignant in our setting given the findings in Boustan

(2010), who documents that Black inflows to central cities induced white residents to move to

the suburbs. We thus need to verify that, at the county level, the Great Migration did not

systematically lead to white out-migration.

We begin by replicating the analysis conducted above focusing on a larger geographic

unit, the CZ, which typically contained both central cities and the suburbs.24 Table A.9

replicates Table 2, documenting that the effects of the Great Migration on the Democratic

vote share remain statistically significant and become, if anything, larger in magnitude.25 In

Table A.10, we conduct a similar exercise for CORE demonstrations. The precision of our

estimates deteriorates, perhaps not surprisingly given the local nature of such events and the

lower number of observations. However, 2SLS coefficients remain quantitatively very close to

those reported in the county-level specification of Table 3. Tables A.9 and A.10 suggest that

our main results are unlikely to be driven by white out-migration systematically triggered by

Black in-migration.

Next, to more directly inspect the presence of white flight, we replicate the analysis con-

ducted in Boustan (2010) for the counties in our sample. We regress the decadal change in

white population against the corresponding change in Black population. We consider the num-

ber of white and Black residents both to make our analysis directly comparable to that in

Boustan (2010) and because this is the most appropriate specification to examine the migra-

tion response of northern residents (see also Peri and Sparber, 2011, and Shertzer and Walsh,

2019). We report 2SLS results in Panel A of Table 4, presenting the associated first stage in

Panel B.

We start from a parsimonious specification, which only includes interactions between state

and year dummies (column 1). Panel B verifies that the instrument is strong, and the F-

stat is well above conventional levels.26 Turning to Panel A, 2SLS coefficients are positive,

quantitatively small, and imprecisely estimated. In column 2, we include the same set of

controls as in our preferred specification (see Section 5.1 above). Also in this case, Black

24CZs have become the standard measure of “labor markets” in the US since at least Autor and Dorn (2013).They were originally developed by Tolbert and Sizer (1996) using commuting patterns to create clusters ofcounties characterized by strong commuting ties within CZs and weak commuting ties across CZs.

25Panels B and C report, respectively, results for turnout and the first stage.26The point estimate in Panel B indicates that one additional predicted Black migrant is associated with 2.5

more Black residents in the county. The magnitude of the coefficient is smaller than, but in line with, thatreported in Boustan (2010).

17

in-migration is associated with a small, positive, and imprecisely estimated effect on white

population.

The bottom rows of Table 4 report the average 1940 white population and the average

change in Black and white population during the period for the counties in our sample. The

coefficient in column 2 (Panel A) implies that 1,000 more Black residents in a county – or, half

of the average change in Black population over the period – were associated with around 300

more white residents. Considering that, on average, the 1940 white population was 71,000,

this represents a negligible change (0.4% relative to the baseline white population). Columns 3

and 4 show that results are robust to including only counties with baseline urban share of the

population above the sample median (0.356), and to interacting the 1940 urban share of the

population with year dummies. Results are also unchanged when estimating long-difference

regressions (Table A.11).

Two observations help reconcile our findings with those in Boustan (2010). First, Boustan

(2010) focuses on central city to suburb migration, fixing city boundaries to 1940, whereas

we consider counties. Second, the (historical) central city-suburb divide does not overlap

with county boundaries; hence, the reallocation of white population between central cities and

suburbs was likely absorbed within counties. Table A.12 provides evidence consistent with this

conjecture. Specifically, in columns 1 and 2, we restrict attention to the 117 counties that are

included in the MSAs considered in Boustan (2010), and replicate our previous analysis. Also

in this sample, the Great Migration had no effect on changes in white population. In columns

3 and 4, we instead focus on central cities, and define the dependent variable as the change in

white population living there. Now, as in Boustan (2010), Black in-migration becomes strongly

associated with white out-migration.27

This analysis is reassuring because indicates that, at the county level, Black in-migration

did not trigger white out-migration. However, one may still be concerned that the Great

Migration led to selective white departures, which altered the composition of white residents.

To address this possibility we proceed as follows. First, we collect data from the 5% sample

of the 1960 Census of Population and from the full count Census of 1940.28 Given the limited

sample size and geographic coverage of the 1960 Census, we aggregate the data to the CZ and

conduct the analysis at this level.29 Next, restricting attention to white men above the age

of 18 and not enrolled in school, we create the share of residents in this group who were: i)

high skilled; ii) employed in manufacturing; iii) in the labor force; iv) homeowners; and v)

above the age of 65. Finally, we estimate long difference regressions, where the 1940 to 1960

change in each of the variables above is regressed against the corresponding (instrumented)

change in the Black share, including our preferred set of controls. 2SLS and first stage results

are reported, respectively, in Panels A and B of Table A.15. The coefficient on the change in

the Black share is always imprecisely estimated, quantitatively small, and does not display any

consistent pattern across outcomes.

27Results are unchanged when estimating long difference regressions (Table A.13).28For 1950 and 1970, only a 1% sample is available, limiting substantially the geographic coverage of the

datasets.29Not all CZs spanning the counties in our sample can be identified in the 1960 Census. Table A.14 shows

that restricting attention to the sample of CZs that can be identified in the 1960 Census leaves our politicalresults unchanged.

18

Using the approach just described, we also show that Black inflows did not increase labor

market competition for white residents (Tables A.16 and A.17 ).30 This confirms existing

evidence that northern labor markets were highly segmented along racial lines, and African

Americans rarely – if at all – directly competed for jobs with whites (Boustan, 2009).

5.2.2 Summary of Additional Robustness Checks

The previous section showed that our findings are unlikely to be influenced by white flight. We

now provide a summary of the additional robustness checks we conducted and that are more

extensively discussed in Appendix C.

First, we verify that the instrument is uncorrelated with three potential pull factors (either

for contemporaneous or for pre-1940 Black settlements): WWII contracts, New Deal spending,

and the vote share of Franklin D. Roosevelt in the 1932 elections (Table C.1).

Second, to address concerns that 1940 Black settlements might be correlated with county-

specific characteristics that may have had a time varying effect on changes in political condi-

tions, we interact period dummies with several 1940 county characteristics, such as the urban

and the immigrant share, the employment share in manufacturing, the employment to popula-

tion ratio, and county population (Tables C.2 and C.3). Tables C.2 and C.3 also document that

results are unchanged when augmenting the baseline specification with a measure of predicted

industrialization constructed using a Bartik-style strategy that combines the 1940 industrial

composition of non-southern counties with national growth across industries. Moreover, to deal

with the possibility that variation behind the instrument may be disproportionately driven by

specific northern counties-southern states combinations (Goldsmith-Pinkham et al., 2020), we

replicate our results by interacting year dummies with the share of Blacks from each southern

state (Figures C.1, C.2, and C.3).

Next, we show that there is no correlation between pre-period changes in the outcomes

of interest and the instrumented change in the Black share (Tables C.4 and C.11). Finally,

we provide evidence that our results are: i) robust to excluding potential outliers, estimating

different specifications, and measuring electoral outcomes in different ways (Tables C.5, C.6,

C.7, and C.8); ii) unlikely to be driven by the simultaneous inflow of southern white migrants

(Tables C.5 and C.6); and iii) unchanged when constructing a version of the instrument that

only exploits variation in “push factors” across southern counties (Tables C.9 and C.10).31

5.3 Mechanisms

Section 5.1 shows that the Great Migration increased support for civil rights among northern

voters. In this section, we first provide different pieces of evidence that Black in-migration

increased support for civil rights among some segments of the white electorate (Section 5.3.1),

and explore two channels through which this might have happened (Section 5.3.2). Next, we

show that (within county) residential segregation may have increased support for civil rights by

30As before, we restrict attention to men of age 18 or more who were not in school. Since data on employment,occupation, or wages are separately available by race (and gender or age) only from micro-censuses, we focus onyears 1940 and 1960, and conduct the analysis at the CZ level.

31Appendix C also performs additional robustness checks on CD results presented in Section 6.

19

making it easier for whites to create independent school districts, thereby lowering the potential

for racial animosity (Section 5.3.3).

5.3.1 Black in-Migration and Whites’ Attitudes Towards Civil Rights

Bounds on whites’ voting behavior. As already noted in Section 5.1, the fact that changes

in the Black share increased the Democratic vote share by more than one for one points to the

importance of changes in northern voters’ behavior. While northern Black residents certainly

played a role, in order to explain our previous estimates, at least some white residents had to

switch to the Democratic Party too. In Figure A.10, we provide bounds on the number of white

voters who had to switch from the Republican to the Democratic Party in order to match the

2SLS coefficient estimated in column 6 of Table 2 for the average county in our sample, under

different assumptions on turnout and voting preferences of African Americans.32

The red square on top of the four bars represents the number of votes for the Democratic

Party that are implied by our coefficient following one percentage point increase in the Black

share for the average county in our sample. The light blue (resp. dark blue) parts of each bar

represent the number of votes from northern Black (resp. white) residents in 1940, while the

grey area refers to votes for the Democratic Party cast by Black migrants. The area between

the top of the bar and the red square represents the number of voters – Blacks or whites – that

would have to switch from the GOP to the Democratic Party in order to explain our results. If

Black turnout was equal to the country-level average (70%), and 70% of all Blacks (migrants

and northern residents) voted for the Democrats (Bositis, 2012), our estimates would not be

fully explained by the behavior of Black voters, and we would also need some white “switchers”

(third bar from the left).33 For Black voters to fully explain our results, we would need to make

more extreme assumptions, such as above-average turnout or near universal support for the

Democrats among Black migrants (first and second bar from the left).

Clearly, this exercise is not meant to compute the exact number of white and Black voters

switching to the Democratic Party for each new Black migrant. Our goal is instead to show

that, under reasonable assumptions, Black migrants’ behavior alone is not sufficient to explain

the increase in the Democratic vote share estimated above, and that at least some northern

voters – both Blacks and whites – started to vote for the Democrats. In what follows, we

provide additional evidence consistent with this idea.

Additional evidence from CORE demonstrations. Section 5.1.2 above documented

that counties receiving more African Americans experienced a larger increase in the frequency

of pro-civil rights demonstrations organized by the CORE. While the CORE was an inter-

racial organization founded by both Black and white students from the University of Chicago

(Gregory and Hermida, 2019), our previous analysis remained silent on whether white activists

32When performing this exercise, we fix turnout, assuming that the inflow of Black migrants can change thepreferences of existing voters but does not alter the number of northern residents (of either race) voting. Thisassumption is consistent with our previous results for turnout (Table 2, Panel B).

33Specifically, under this scenario and the one in the fourth bar from the left, where we assume that Blackturnout was equal to 50%, approximately 2 white voters would have to switch for every ten Black migrants toexplain the 2SLS coefficient estimated in Table 2.

20

joined the demonstrations. We now make progress on this specific point by exploiting the fact

that, for a subset of CORE events, we were able to identify the race of participants.

In column 7 of Table 3, we estimate our preferred 2SLS specification considering as de-

pendent variable the change in the probability of CORE demonstrations with both Black and

white participants. Notably, this represents a (very conservative) lower-bound for the proba-

bility that whites joined pro-civil rights demonstrations, since only for approximately 40% of

CORE events participants’ race was reported, and we define a protest as having white partici-

pants only when their presence was explicitly reported. Although the point estimate is half as

that for the baseline specification (column 6), it remains positive and statistically significant,

with a p-value of 0.058.

Evidence from historical survey data. We complement the previous results by exploiting

historical survey data from the ANES and Gallup. As noted above, neither dataset reports

the county, but only the state of residence of respondents, and questions on racial views are

available only from the end of the 1950s.34 Thus, we estimate cross-sectional regressions,

correlating whites’ racial attitudes and political preferences in surveys conducted in years close

to the CRA with the (instrumented) 1940-1960 change in the Black share in their state of

residence. Because of the cross-sectional nature of this exercise, we acknowledge that the

evidence should be interpreted as suggestive.

To limit concerns about omitted variable bias, we always include survey year and Census

region fixed effects, a set of 1940 state level characteristics (manufacturing share, urban share,

share of unionized workers, Black share, indicator for Democratic incumbency in Congressional

elections), and individual controls (gender, marital status, and fixed effects for both age and

education).35 We always restrict attention to white respondents living in non-southern states.

We start by presenting results from the ANES in Table 5, where the dependent variable

is a dummy equal to 1 if support for civil rights was considered by respondents as one of the

most important issues for the country in 1960 and 1964 (see Appendix D for more details). In

the same survey years, respondents were also asked whether they opposed school and housing

or working space integration. Combining these questions, columns 1 to 3 of Table 5 first verify

that considering civil rights as one of the most important problems is strongly, negatively

correlated with opposition to racial integration. We thus interpret the dependent variable in

Table 5 as a proxy of support for racial equality. Next, columns 4 and 5 turn to the relationship

between the 1940-1960 change in the Black share and the most important issue dummy for

civil rights, using OLS and 2SLS respectively.

2SLS estimates indicate that white respondents living in states that received more Blacks

34The first year for which ANES data are available at the county level is 1978 – well after the period consideredin our analysis. Although some of these questions were asked also after 1964, we refrain from using any post-CRA survey dataset because of the potential direct effect of the bill on whites’ racial attitudes (Kuziemko andWashington, 2018; Wheaton, 2020).

35Since party identification and union membership may be endogenous to Black inflows, we do not includethem in our baseline specification. However, adding these controls does not change any of our results. Gallup didnot ask questions about marital status in the survey waves for which we could collect data on racial attitudes.Thus, this control is omitted when focusing on Gallup (see also Appendix D for more details). Results are alsorobust to including further 1940 state level controls such as the immigrant share, the share of unskilled workers,and other socioeconomic or political variables.

21

between 1940 and 1960 were significantly more likely to consider civil rights as one of the

most important issues. This relationship is quantitatively large: according to the coefficient

reported in column 5, one percentage point increase in the Black share between 1940 and 1960

is associated with a 1.8 percentage points (or, 17%) higher probability of considering civil

rights one of the most important problems in the two ANES surveys asked before the CRA. In

column 6 of Table 5, we exploit the fact that the ANES asked respondents not only their state

of residence, but also their state of birth. We restrict attention to whites who, at the time of

the survey, lived in the same state where they were born. Reassuringly, despite the reduction

in sample size, the coefficient on the change in the Black share remains statistically significant,

positive, and similar to that of column 5.36

Next, we focus on the relationship between the Great Migration and whites’ political pref-

erences. Focusing on survey waves between 1956 and 1964 and estimating 2SLS regressions,

Table A.19 documents that white respondents living in states that received more Black mi-

grants between 1940 and 1960 were significantly more likely to vote for (column 1) and identify

with (column 4) the Democratic Party. This relationship becomes slightly stronger when re-

stricting attention to non-movers (columns 2 and 5) and when considering only 1964 (columns

3 and 6). The fact that the relationship between Black inflows and whites’ support for the

Democratic Party was higher in 1964 is consistent with the civil rights issue featuring more

prominently during the year that led to the passage of the CRA.

Finally, Table A.20 verifies that similar patterns hold when using data from Gallup in the

1963 and 1964 waves. White respondents living in states that received more Black migrants

were less likely to object to the idea of racial mixing in schools (column 1), more likely to state

that they would vote for a Black president were their party to nominate one (column 2), and

more supportive of the CRA (column 3). There is instead no relationship between the Great

Migration and respondents’ views on whether the process of racial integration was proceeding

at the right pace (column 4).37

5.3.2 Unpacking the Channels Behind Whites’ Support for Civil Rights

The previous section provided different pieces of evidence that the Great Migration increased

support for civil rights among some segments of the white electorate. This might have hap-

pened for at least two, non-mutually exclusive, reasons. First, as envisioned by Myrdal (1944),

exposure to Black migrants might have increased whites’ awareness of the brutal conditions

prevailing in the South, in turn fostering demand for more racial equality in the region. In

addition, as predicted by the “contact hypothesis” (Allport, 1954), inter-group contact might

have reduced negative stereotypes and prejudice held by whites, changing their attitudes to-

wards Blacks. Second, economic and political incentives might have favored the formation

of a class-based cross-race coalition between white and Black Americans. This possibility is

36Table A.18 confirms the lack of correlation between Black in-migration and whites’ mobility across statesby regressing a dummy equal to 1 if the respondent lived in a state other than her state of birth against the(instrumented) 1940-1960 change in the Black share in the state of residence. This holds for different subsets ofrespondents (see the bottom row of the table).

37The number of observations varies substantially across questions, since some of these were asked repeatedlyduring 1963 and 1964. This was particularly true for the question on the pace of racial integration (column 4).

22

discussed, among others, in Adams (1966), Schickler (2016), and Sugrue (2008), who argue

that the labor movement in northern and western urban areas saw the Great Migration as a

window of opportunity to strengthen its political clout.

Clearly, economic and social factors may have interacted. For instance, frequent contacts

in an environment that promoted a shared, class-based, identity and where Black and white

workers had common goals reduced the barriers traditionally in place between members of

the majority and of the minority group, favoring the formation of a racially diverse social

coalition.38 Once a liberal coalition on both economic and racial issues was formed, its members

were likely in a better position than before to organize effective political campaigns. In turn,

the formation of a pro-Democratic coalition, favored by Black arrivals to northern cities, might

have put the Democratic Party in an advantageous position relative to the Republican Party.

In what follows, we focus on pro-civil rights demonstrations as our main indicator for voters’

demand for racial equality. We exploit heterogeneity in the 1940 characteristics of counties in

order to understand which segments of the white electorate increased their support for civil

rights.39

Social and cultural forces. According to the information mechanism envisioned by Myrdal

(1944), the Great Migration should have led to a larger progressive shift in whites’ racial

attitudes in counties that were already more socially progressive. To test this idea, we estimate

our preferred 2SLS specification, splitting the sample above and below the median of different

proxies for progressive attitudes in the local electorate. Figure 2 plots the coefficient on the

change in the Black share for counties above (orange bars) and below (blue bars) the median

of each proxy for socially progressive attitudes. To ease the interpretation of results, we always

rescale the variables along which we perform the sample splits so that higher (resp. lower)

values refer to socially more (resp. less) progressive counties.40

First, we consider the discrimination index constructed in Qian and Tabellini (2020) using

historical data from a variety of sources, such as local presence of the KKK and the lynching

of Black Americans up to 1940. We find that the effects of the Great Migration were an order

of magnitude larger in counties with lower historical discrimination. The same pattern, though

less pronounced, is evident when splitting counties as belonging to states with (blue bars)

and without (orange bars) miscegenation laws (Dahis et al., 2020). Second, we find that pro-

civil rights demonstrations increased more in places with a larger 1940 immigrant population.

Higher immigrant share captures tolerance of the local population, as the foreign-born are more

likely to sort into areas that are open to minorities. This result is also in line with historical

accounts noting that minorities were more likely to join African Americans in their demand

for racial equality (Schickler, 2016; Sugrue, 2008).41

38Using recent data, Frymer and Grumbach (2020) find that white union members hold significantly moreliberal attitudes towards minorities in the United States.

39Due to the limited number of events for which we have information on the race of participants, we are forcedto perform the heterogeneity analysis using data on all pro-civil rights demonstrations.

40The corresponding estimates are reported in Table A.21. Given that the values of the F-stat are often belowconventional levels, results should be interpreted with caution.

41Exceptions may be the Irish and the Poles, who were more likely to compete for status and jobs with BlackAmericans, and who had been historically antagonist to the latter (Bailer, 1943; Sugrue, 2014). In unreported

23

Finally, we explore how results are mediated by the characteristics of the white electorate.

The last two sets of bars in Figure 2 split the sample according to the 1940 share of whites

who were: i) young (defined as individuals of age 35 or younger); and ii) born in the South.

The former proxy is motivated by the fact that young individuals are more open to social

change (Acemoglu et al., 2020; Inglehart, 2015; Miles, 2000; Parker et al., 2016). The latter

is motivated by the fact that southern born whites were less likely to support racial equality

(Bailer, 1944; Kuziemko and Washington, 2018). Even though coefficients are less precisely

estimated than before, they indicate that CORE demonstrations increased more in counties

where the white population was more progressive.

Political and economic forces. The second mechanism that may have increased whites’

support for civil rights is that the labor movement strategically incorporated African Americans

in order to increase its political strength. This, in turn, led to a cross-race coalition pushing

for both economic and racial liberalism (Schickler, 2016). As before, we exploit baseline cross-

county heterogeneity, estimating the effects of Black in-migration on pro-civil rights protests

separately for counties above and below different proxies for the presence and the strength

of organized labor, or for its incentives to incorporate African Americans. We plot 2SLS

coefficients on changes in the Black share in Figure 3, always defining the variables over which

we conduct the split so that higher (resp. lower) values refer to stronger (resp. weaker) presence

of, or incentives for, unions to support the civil rights movement.42

The first set of bars from the left shows that pro-civil rights demonstrations were more likely

to occur in counties where political competition – defined as one minus the absolute value of the

margin of victory in 1940 Congressional elections – was higher. Instead, in counties with less

competitive elections, the Great Migration did not have any effect on pro-civil rights protests.

This finding is consistent with labor unions and the Democratic Party having stronger incentives

to coordinate events where the Black vote was more valuable. Precisely in these areas, a better

organized political machine could have made a difference in attracting and mobilizing pivotal

voters (Cantoni and Pons, 2020; Pons, 2018). Clearly, this argument is not confined to the

role of labor unions, but is true of organizational incentives for the civil rights movement more

broadly (McAdam, 1982).

Next, we turn to variables more directly related to the presence of labor unions. The second

and third set of bars from the left split the sample above and below the median of the share of

white men employed in manufacturing and in the unskilled sector, respectively. The surge in

civil rights demonstrations was concentrated in counties with a higher share of white workers in

the two sectors where unions were most widespread (Bailer, 1944; Farber et al., 2018). On the

contrary, Black in-migration had a negative, quantitatively small, and imprecisely estimated

impact on support for civil rights in counties with a low share of white men employed in

manufacturing or who were unskilled.

Abundant historical evidence shows that, among labor unions, the CIO was the most en-

results, we examined heterogeneity depending on the local presence of Irish and Pole first or second generationimmigrants, but we did not find any statistically or economically significant difference for the effects of the GreatMigration.

42We report the corresponding estimates in Table A.22.

24

gaged one with civil rights, and often worked side by side with inter-racial organizations like

the CORE and with NAACP leaders. Thus, one would expect Black in-migration to have a

stronger effect in places with a higher share of unionized workers who were members of the CIO.

Since CIO membership data are not available at the county level, we use the dataset assembled

by Troy (1957) to construct the share of unionized workers affiliated with the CIO in 1939 in

each state. Splitting the sample in counties belonging to states with CIO unionization rates

above and below the median, we find that the effects of the Great Migration were stronger,

although not statistically different, in areas where the CIO was relatively more important.

The last set of bars of Figure 3 tests a corollary of the “economic incentives” mechanism:

labor unions, and northern white workers more generally, should have been more willing to

accept Blacks and support racial equality when labor markets were tighter and economic op-

portunities more abundant. As conjectured by Allport (1954) and Blalock (1967), inter-group

contact is more likely to lead to cooperation when it happens in contexts with no competition

over scarce resources.43 Consistent with this prediction, the last bars of Figure 3 split the

sample in counties with predicted economic growth above and below the median, and show

that Black in-migration was associated with more pro-civil rights demonstrations only in the

former.44 These findings are consistent with a number of anecdotal accounts, such as Bailer

(1943) and Sugrue (2014), who point out that whites’ backlash was more likely to emerge dur-

ing economic downturns. They are also in line with the electoral results presented in Table A.6

above, which documented that the Great Migration had no effect on the Democratic vote share

in the 1950s – a decade characterized by slack labor markets and economic downturns.

We conclude this section by providing two more pieces of evidence on the strategic incentives

of labor unions using the ANES data. First, we investigate the heterogeneity behind results

presented in column 5 of Table 5, which showed that the Great Migration was positively

correlated with the probability that white respondents considered the civil rights issue as the

most important problem for the country. Figure A.11 documents that these patterns are

significantly stronger for union members (second, Black bar) and for Democrats (fourth, blue

bar).45 If anything, for Republicans we observe a negative, albeit not statistically significant,

relationship between state level increases in the Black share and support for civil rights. Since

union status and partisanship may be endogenous to the Great Migration, results in Figure A.11

should be viewed as merely suggestive. However, they paint a picture coherent with our

previous discussion.

Second, we exploit the fact that, in 1964, the ANES included questions on respondents’

“feeling thermometers” towards different political, demographic, and socioeconomic groups,

with higher values reflecting warmer feelings towards a group. 2SLS estimates in Table A.24

show that Black inflows were positively associated with feelings towards Democrats (column

1) and labor unions (column 2). The 1940-1960 change in the Black share was also positively

correlated with whites’ feelings thermometers towards African Americans (column 3) and the

43Several papers have documented that scapegoating and violence against minorities are more likely to happenduring times of hardship (Grosfeld et al., 2020; Oster, 2004; Voigtlander and Voth, 2012).

44We predict economic growth using a simple Bartik-style approach, interacting 1940 industry shares atthe county level with national growth rates of each industry in each subsequent decade, and averaging acrossindustries (for each decade).

45See Table A.23 for the 2SLS coefficients plotted in Figure A.11.

25

NAACP (column 4), even though these results are not statistically significant.

5.3.3 Residential Segregation and Independent Local Governments

The lack of (between county) white flight and the higher support for civil rights among some

white voters do not imply that white residents welcomed Black migrants into their neighbor-

hoods. As prior research shows, Black migration may have increased racial segregation, and

efforts of whites to avoid sharing public goods with Blacks (Alesina et al., 1999, 2004). We

confirm these patterns in our setting. We document that this mechanism may have amplified,

rather than dampening, the positive effect of the Great Migration on demand for civil rights.

On the one hand, residential segregation was a way to diffuse white animosity caused by Black

migration into white neighborhoods. On the other hand, the ability to segregate may have

sustained and reinforced perceptions among liberal whites that the civil rights legislation was

mostly about the South, and that their privileges would not be significantly eroded.

Table 6, column 1, provides evidence consistent with these ideas by focusing on pro-civil

rights demonstrations. Panel A replicates results in Table 3 (column 6), whereas Panels B

and C split the sample in counties with 1940 residential segregation (constructed using the

procedure in Logan and Parman, 2017) below and above the median, respectively. The F-stat

falls below conventional levels in Panel C, and so results should be interpreted with caution.

However, the pattern that emerges is clear: Black in-migration increased the frequency of pro-

civil rights demonstrations only in counties with higher residential segregation. Said differently,

support for civil rights increased more in counties where inter-group contact in the housing

market was lower.

This may have happened because residents of initially segregated counties had little contact

with Blacks, and were able to further isolate themselves from inter-racial tensions that Black

migration may have brought to neighborhoods and housing markets. To achieve this goal,

whites could create more homogeneous local jurisdictions. In columns 2 to 5 of Table 6, we

examine whether the Great Migration increased the number of local jurisdictions using data

from the Census of Government. We replicate the regressions in column 1 focusing on the

change in the (log of) number of: i) total jurisdictions (column 2); ii) school districts (column

3); iii) special districts (column 4); and, iv) municipalities (column 5). In the full sample

(Panel A), Black in-migration had a positive and statistically significant effect on the number

of local jurisdictions (column 2) – a pattern driven entirely by school districts (column 3).

However, this happened only in counties with residential segregation above the median (Panel

C).

One interpretation, consistent with the historical evidence (Sugrue, 2008), is that, since

higher residential segregation lowered the probability that Black and white pupils shared the

same school district, whites’ incentives to create local jurisdictions were stronger in more segre-

gated counties. Coupled with findings in column 1, this suggests that population sorting within

counties and the creation of independent jurisdictions might have reduced potential backlash by

allowing whites to live in racially homogeneous communities, where the probability of sharing

public goods with Blacks was low.

26

6 Legislators’ Behavior

This section studies the impact of the Great Migration on support for civil rights legislation

among members of the House. First, it shows that Congress members representing CDs that

received more Black migrants were more likely to vote in favor of pro-civil rights bills and to

sign discharge petitions to promote racial equality (Section 6.1). Next, it documents that Black

in-migration increased political polarization on racial issues (Section 6.2).

6.1 Ideology Scores and Discharge Petitions

6.1.1 Ideology Scores

We begin the analysis of legislators’ behavior by focusing on the ideology scores from Bateman

et al. (2017), which take more negative (resp. positive) values for more liberal (resp. con-

servative) voting behavior on civil rights bills. Columns 1 to 3 in Table 7 present results for

the change in agnostic ideology scores, stacking the data for the 78-82 and the 82-88 Congress

periods, reporting 2SLS and first stage coefficients in Panels A and B respectively. Following

Autor et al. (2020) and Bonomi et al. (2020), to deal with the potential issue of mean rever-

sion, in addition to the controls included in our preferred specification above, we also add the

interaction between period dummies and the baseline ideology score of legislators. The point

estimate reported in column 1 (Panel A) is negative, but quantitatively small and imprecisely

estimated.

However, when examining the results separately by Congress period, a more nuanced picture

emerges. Black in-migration had a strong, negative effect on the ideology scores of legislators in

the first Congress period (column 2), and a negligible, positive, and not statistically significant

effect in the second period (column 3). While the F-stat falls below conventional levels in

column 2, suggesting that our estimates should be interpreted with some caution, these findings

indicate that legislators’ ideology moved to the left between Congress 78 and Congress 82, and

did not change significantly afterwards. Results are robust to focusing on the constrained

version of the ideology scores (columns 4 to 6).

In our baseline specification, we map the 1940-1950 (resp. 1950-1960) Black in-migration

to the 78-82 (resp. 82-88) Congress period, so as to both have the longest periods without

redistricting and end the analysis with the Congress that passed the CRA. Appendix C.5 verifies

that our findings are robust to different timing conventions. It also shows that there are no

pre-trends, and runs a number of robustness checks to verify that results are not influenced by

strategic gerrymandering of CD boundaries, possibly induced by Black in-migration (Kaufman

et al., 2017).

6.1.2 Signatures on Discharge Petitions

Due to gatekeeping imposed by southern Democrats, civil rights bills were unlikely to reach the

floor of the House, unless northern legislators were willing to undertake non-standard actions.

Discharge petitions represent the best example of such non-conventional tools at disposal of

non-southern legislators (Pearson and Schickler, 2009). Since there are not enough discharge

27

petitions filed during the 82-88 Congress period, we focus on the 1940s, when several discharge

petitions were filed and signed on the same topics – fair employment legislation (FEPC), the

poll tax, and anti-lynching legislation – both at the beginning and at the end of the decade.

Although all three topics featured prominently in the political debate during the 1940s, some

differences existed between them. First, the salience of both the poll tax and anti-lynching

legislation gradually declined relative to that of anti-discrimination employment legislation

during the 1940s. For instance, the last discharge petition on either the poll tax or anti-

lynching legislation was filed during the 80th Congress, whereas discharge petitions on FEPC

were filed also in the early 1950s (Table A.5). Second, anti-lynching legislation and, to a lesser

extent, the abolition of the poll tax almost exclusively concerned racial relations in the South;

conversely, employment protection legislation had a direct, strong impact not only in the South

but also in the North (Sugrue, 2014). For both reasons, one may expect FEPC to be the most

relevant category, where northern legislators may have tried to signal their (pro-civil rights)

stance the most.

Figure 4 plots the 2SLS point estimate (with 95% confidence intervals) for the effects of

changes in the Black share on the change in the probability of signing a discharge petition on any

of the three topics (first dot) and then on each topic separately (second to fourth dots from the

left). Black in-migration had a positive and statistically significant effect on the probability of

signing a discharge petition on all topics. Consistent with the previous discussion, these effects

are larger and more precisely estimated for FEPC legislation than for other categories.46

Table A.27 presents additional, suggestive evidence estimating a “levels on changes” specifi-

cation. The key limitation of this strategy is that unobservable CD fixed characteristics cannot

be controlled for. However, it allows us to provide evidence also for the second Congress pe-

riod. In columns 1 to 3 (resp. 4 to 6), the dependent variable is the number of signatures

on discharge petitions per legislator signed (resp. the share of petitions signed prior to the

50th signature) during the 78-82 and the 83-88 Congress periods.47 2SLS coefficients show that

Black in-migration was positively associated with a higher number of signatures per legislator.

Moreover, legislators representing CDs that received more African Americans were more likely

to sign petitions earlier in the process – a pattern consistent with higher legislators’ support

for civil rights legislation (Schickler, 2016).

46Table A.25 reports the coefficients associated with Figure 4. Since the KP F-stat is below conventionallevels, results should be interpreted with some caution. The change in the probability of signing a petitionon FEPC, anti-lynching legislation, and the poll tax is taken over Congresses 81 to 78, 80 to 77, and 79 to77 respectively. Since petitions on the three topics were not always signed in the same Congress year andwere not always comparable with each other (see Table A.5), we checked the robustness of our results using anumber of alternative time windows. Reassuringly, they always remained similar to those presented in Figure 4.Results in Figure 4 and in Table A.25 are obtained from a slightly larger number of CDs relative to those forwhich legislators’ ideology is available. Restricting attention to these CDs leaves our results unchanged (seeTable A.26).

47Only one petition was filed during Congress 82, obtaining merely 16 signatures (Table A.5). For this reason,defining the end (resp. the beginning) of the first (resp. second) Congress period with Congress 82 or Congress83 makes no difference.

28

6.2 Heterogeneous Effects

6.2.1 Within vs Between Party Changes

The shift in legislators’ ideology to the left documented in Table 7 might come from two,

non-mutually exclusive forces. First, it may reflect changes taking place between parties,

if Republican legislators were replaced by Democratic legislators. Second, it might capture

changes taking place within parties, if the ideology of Congress members of the same party

shifted towards more liberal positions.

To disentangle these mechanisms, we create four separate dummies for each possible party

transition experienced by a CD between the beginning and the end of a Congress period – from

Republican to Democratic, from Republican to Republican, from Democratic to Democratic,

and from Democratic to Republican. Next, we interact such dummies with the change in the

Black share, and include all interactions in the same specification (in addition to the direct

effect of Black in-migration). The omitted category is the interaction between the change in

the Black share and the Democratic-to-Democratic dummy. Thus, coefficients on the various

interaction terms should be interpreted as the effects of Black in-migration in a CD undergoing

a given transition, as compared to CDs that were Democratic both at the beginning and at

the end of the Congress period.

We report 2SLS estimates from the baseline stacked first difference regression in Table 8, for

the agnostic and the constrained version of the ideology scores in columns 1 and 2 respectively.48

The main effect of Black in-migration is negative and marginally statistically significant (with

a p-value of 0.056). Focusing on the interaction terms, the coefficient on the “Republican

to Democratic” transition is negative and marginally significant, indicating that the Great

Migration produced a more pronounced shift towards the left in CDs that switched from the

Republican to the Democratic Party – evidence of a between party adjustment. Perhaps more

interestingly, the coefficient on the interaction for the “Republican to Republican” transition is

positive and statistically significant. This implies that Black in-migration induced Republican

legislators to become less liberal on racial issues in CDs that were Republican both at the

beginning and at the end of the period. The within party adjustment taking place in GOP

dominated CDs further suggests that the average effects estimated above may mask between

party polarization on racial issues.

6.2.2 Political Polarization

To examine the possibility that the Great Migration increased political polarization, we follow

the approach used in Autor et al. (2020) and Tabellini (2020) for trade and immigration

respectively. We define liberal (resp. moderate) Democrats those legislators with an ideology

score below (resp. above) the median score for Democrats in Congress 78. Likewise, moderate

(resp. conservative) Republicans are defined as Congress members with an ideology score

below (resp. above) the median score for Republicans in Congress 78. Table 8 estimates our

48Since there are now four endogenous regressors and four instruments, we report the AP F-stat associatedwith each instrument, as suggested by Angrist and Pischke (2008). In two out of four cases, these are belowconventional levels, likely due to the highly demanding nature of the exercise.

29

baseline stacked first difference specification, using as dependent variable the change in the

probability of electing a liberal Democrat (column 3), a moderate Democrat (column 4), a

moderate Republican (column 5), and a conservative Republican (column 6).

2SLS coefficients document that Black in-migration had a positive, statistically significant

effect on the probability of electing a liberal Democrat. The remaining coefficients are impre-

cisely estimated, but suggest that this was accompanied by a reduction in the probability of

electing moderate legislators (either Democratic or Republican). If anything, the point esti-

mate for the probability of electing a conservative Republican is positive, albeit small and not

statistically significant.

Section 6.1.1 above showed that the Great Migration had a strong, negative effect on the

ideology scores of legislators in the 1940s, but a positive – and in some specifications statistically

significant (see Table C.13) – one in the 1950s. Motivated by this evidence, in Figure 5, we

re-estimate the regressions reported in columns 3 to 6 of Table 8 separately for each of the

two decades in Panels A and B respectively.49 The pattern arising is rather striking. In the

1940s, Black in-migration had a strong, positive effect on the probability of electing a liberal

Democrat. This was accompanied by a steep reduction in the probability of electing both

moderate Democrats and conservative Republicans. If anything, the probability of electing a

moderate Republican increased with Black inflows, even though results are not statistically

significant. These findings reflect the combination of between and within party adjustments

discussed above.

In the 1950s, instead, Black in-migration was associated with a statistically significant

increase in the probability of electing a conservative Republican, mirrored by the corresponding

decline in the probability of electing a moderate Republican. The effects of Black in-migration

on the probability of electing Democrats with different ideological stances are very small in size

and imprecisely estimated. The right-ward shift of Republican legislators during the 1950s may

have been motivated by strategic considerations, as the GOP tried to win the votes of whites

who were becoming increasingly concerned about the racial mixing of their neighborhoods

(Sugrue, 2014).

Since quantitatively results in Panel A are larger than those in Panel B, on average, leg-

islators’ ideology moved to the left. However, when inspecting these dynamics more carefully,

polarization becomes evident. These findings are also consistent with the possibility that lo-

cal responses to the Great Migration were partly influenced by national considerations: even

though the Democratic Party “lost the South” by openly promoting the civil rights agenda

(Kuziemko and Washington, 2018), this strategy might have been instrumental to win urban

areas of the West and the North. At the same time, the Republican Party might have tried

to strengthen its conservative position at the national level, so as to attract dissatisfied white

voters leaving the Democratic Party in the South.

49Table A.28 reports the 2SLS and first stage coefficients associated with Figure 5.

30

7 Conclusions

The Great Migration of African Americans was one of the largest episodes of internal migration

in American history. Between 1940 and 1970, more than 4 million Blacks left the US South for

northern and western destinations. During this same period, the civil rights movement strug-

gled and eventually succeeded to eliminate formal impediments to Black political participation

and to remove (at least) de jure racial segregation. In this paper, we study the effects that

Black in-migration had both on voters’ demand for racial equality and on legislators’ support

for civil rights legislation.

Using a version of the shift-share instrument (Card, 2001; Boustan, 2010), we show that

Black in-migration increased the Democratic vote share in Congressional elections and raised

the frequency of pro-civil rights demonstrations. Our estimates suggest that these effects were

at least in part due to the behavior of white voters, who also joined civil rights protests.

We corroborate this interpretation using historical survey data and examining patterns of

heterogeneity across counties. Next, we document that Congress members representing CDs

that received more African Americans became more liberal on racial issues, and more actively

supported civil rights legislation. These average effects, however, mask substantial polarization

between parties on racial issues.

Our paper complements the existing literature on the Great Migration, which has, especially

in recent times, emphasized the long run, negative impact that the latter had on both racial

residential segregation and economic mobility for African Americans. Our findings, instead,

paint a more nuanced picture. They indicate that, as predicted by Gunnar Myrdal back in

1944, Black in-migration to the US North and West was instrumental for the development of

the civil rights movement – the social movement that was of historical importance to reduce

political, social, and economic racial inequality in the United States.

When contrasted with other works on the political effects of migration, our results raise

an intriguing set of questions. Under what conditions can migration and inter-group contact

more broadly lead to the formation of cross-group coalitions? When, instead, is backlash from

original residents more likely to prevail? In the specific context of the Great Migration and of

the civil rights movement, our evidence suggests that cross-race cooperation can emerge when

individuals belonging to different groups share similar (in this context, more liberal) values,

or when group members interact in domains, such as the working environment, where the

emergence of a common identity and the existence of common goals can sustain political or

social coalitions. At the same time, contact in other domains, such as residential markets and

a more private sphere, might lead to inter-group conflict and majority backlash.

31

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Figures and Tables

Figure 1. Change in the Black Share across US Counties, 1940 to 1970

Notes: The map plots the change in the share of Blacks in the population between 1940and 1970 for the non-southern counties in our sample.

Figure 2. Heterogeneity by County Characteristics - Social and cultural forces

Notes: The bars report the marginal effect of changes in the Black share (withcorresponding 95% confidence intervals) on the change in the Democratic vote sharefor counties with each 1940 variable above (resp. below) the sample median inorange (resp. blue). Section 5.3.2 describes how each variable is constructed. SeeTable A.21 for the coefficients and standard errors corresponding to the graph.

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Figure 3. Heterogeneity by County Characteristics - Political and economic forces

Notes: The bars report the marginal effect of changes in the Black share (withcorresponding 95% confidence intervals) on the change in the Democratic vote sharefor counties with each 1940 variable above (resp. below) the sample median inorange (resp. blue). Section 5.3.2 describes how each variable is constructed. SeeTable A.22 for the coefficients and standard errors corresponding to the graph.

Figure 4. Change in Signatures on Discharge Petitions

Notes: The figure plots the 2SLS coefficient (with corresponding 95% confidenceintervals) for the effects of the 1940-1950 change in the Black share on the corre-sponding change in the number of signatures on discharge petitions per legislator.The first dot on the left (“All”) includes discharge petitions on employment protec-tion legislation (FEPC), to promote anti-lynching legislation, and to abolish the polltax. The three remaining dots refer to each of the three issues. Results (both forOLS and 2SLS) and details on the specification are reported in Table A.25.

40

Figure 5. Black in-Migration and Political Polarization

Panel A: 1940s

Panel B: 1950s

Notes: Each bar reports 2SLS coefficients (with corresponding 95% confi-dence intervals) for the effect of changes in the Black share on the changein the probability of electing a member of the House with the correspondingpolitical orientation between Congress 78 and Congress 82 (Panel A) andbetween Congress 82 and Congress 88 (Panel B). The ideology indicatorsare defined in the main text (Section 6.2.2).

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Table 1. Summary Statistics

Variables Mean Median St. Dev. Min Max Obs

Panel A. 1940 Levels

Black Share (County) 3.60 2.10 0.042 0 46.50 1,139

Black Share (CD) 6.80 7.20 0.047 0 25.40 286

Democratic Vote Share 45.757 49.10 14.564 0 85.00 1,139

Turnout 68.938 69.20 8.479 23.00 97.60 1,139

Civil Rights Scores -0.872 -0.811 0.712 -2.008 1.431 286

Panel B. Changes

Black Share (County) 1.789 0.87 2.303 -4.93 7.537 3,418

Black Share (CD) 5.243 6.686 2.450 -0.808 10.205 571

Democratic Vote Share 1.695 1.80 6.189 -19.267 52.17 3,418

Turnout -6.409 -6.433 3.33 -19.20 9.70 3,418

Civil Rights Scores 0.068 0.065 0.375 -1.177 1.284 571

Notes: The sample includes a panel of the 1,139 non-southern US counties (see Table A.1 for our definition ofsouthern states) for which electoral returns in Congressional elections are available for all Census years between1940 and 1970, and with at least one African American resident in 1940. When relevant, county variablesare collapsed at the Congressional District level, fixing boundaries to Congress 78 as explained in the text.Democratic vote share and turnout refer to Congressional elections, and civil rights scores are the ideologyscores from Bateman et al. (2017). Panel A presents 1940 values, while Panel B reports decadal changes foreach of the variables.

42

Table 2. Congressional Elections

(1) (2) (3) (4) (5) (6) (7)

OLS OLS OLS 2SLS 2SLS 2SLS 2SLS

Panel A. Democratic Vote Share (1940 Mean: 45.89)

Change Black 0.628∗∗∗ 0.607∗∗∗ 0.769∗∗∗ 0.839∗∗∗ 1.165∗∗∗ 1.650∗∗∗ 2.062∗∗∗

Share (0.099) (0.107) (0.131) (0.144) (0.197) (0.286) (0.476)

Panel B. Turnout (1940 Mean: 68.95)

Change Black -0.262∗∗∗ -0.292∗∗∗ -0.268∗∗∗ 0.020 0.194 0.390∗ 0.883∗∗

Share (0.098) (0.092) (0.086) (0.140) (0.172) (0.235) (0.371)

Panel C. First Stage

Predicted Change Black 1.186∗∗∗ 1.357∗∗∗ 1.148∗∗∗ 0.778∗∗∗

Share (0.267) (0.308) (0.311) (0.245)

Specification FD FD FD FD FD FD LD

1940 Black Share X X X X X

1940 Dem incumbent X X X

F-stat 19.69 19.36 13.65 10.13

Observations 3,418 3,418 3,418 3,418 3,418 3,418 1,138

Notes: The sample includes the 1,139 non-southern US counties (see Table A.1 for the definition of southernstates) for which electoral returns in Congressional elections are available for all Census years between 1940and 1970, and with at least one African American resident in 1940. The table reports stacked first differenceregressions in columns 1 to 6, and long difference regressions in column 7. The dependent variable is the decadalchange in the Democratic vote share (resp. turnout) in Congressional elections in Panel A (resp. Panel B).Panel C reports the first stage associated with 2SLS regressions. Columns 1 to 3 estimate equation (1) in thetext with OLS, while remaining columns report 2SLS estimates. The main regressor of interest is the change inthe Black share, which is instrumented with the shift-share instrument described in equation (2) in the text fromcolumn 4 onwards. All regressions are weighed by 1940 county population, and control for state by period fixedeffects. 1940 Black share (resp 1940 Dem dummy) refers to interactions between period dummies and the 1940Black share (resp. a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicansvote share). F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the countylevel, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

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Table 3. CORE Demonstrations

Dep. Variable Change in 1[Pro-Civil Rights Demonstration]

(1) (2) (3) (4) (5) (6) (7)

OLS OLS OLS 2SLS 2SLS 2SLS 2SLS

Panel A. Main Estimates

Change Black 0.036∗∗∗ 0.027∗∗∗ 0.025∗∗∗ 0.068∗∗∗ 0.048∗∗∗ 0.047∗∗∗ 0.022∗

Share (0.006) (0.006) (0.006) (0.010) (0.009) (0.011) (0.012)

Panel B. First Stage

Predicted Change 1.186∗∗∗ 1.357∗∗∗ 1.148∗∗∗ 1.148∗∗∗

Black Share (0.267) (0.308) (0.311) (0.311)

1940 Black share X X X X X

1940 Dem incumbent X X X

White participants X

F-stat 19.69 19.36 13.65 13.65

Observations 3,418 3,418 3,418 3,418 3,418 3,418 3,418

Notes: The sample includes the 1,139 non-southern US counties (see Table A.1 for the definition of southernstates) for which electoral returns in Congressional elections are available for all Census years between 1940 and1970, and with at least one African American resident in 1940. The dependent variable is the change in theprobability of non-violent demonstrations in support of civil rights coordinated by the CORE. Columns 1 to 3estimate equation (1) in the text with OLS, while remaining columns report 2SLS estimates. The main regressorof interest is the change in the Black share, which is instrumented with the shift-share instrument described inequation (2) in the text from column 4 onwards. All regressions are weighed by 1940 county population, andcontrol for state by period fixed effects. 1940 Black share (resp 1940 Dem dummy) refers to interactions betweenperiod dummies and the 1940 Black share (resp. a dummy equal to 1 if the Democratic vote share in 1940 washigher than the Republicans vote share). Column 7 includes only those demonstrations that were joined by atleast some white participants. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clusteredat the county level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

44

Table 4. Black in-Migration and Changes in White Population

Dep. Variable Change in White population

(1) (2) (3) (4)

Panel A. 2SLS

Change Black 0.343 0.318 0.278 0.191

population (0.435) (0.428) (0.410) (0.445)

Panel B. First stage

Predicted Change Black 2.430∗∗∗ 2.442∗∗∗ 2.456∗∗∗ 2.396∗∗∗

population (0.352) (0.339) (0.324) (0.340)

F-stat 47.75 52.01 57.30 49.82

Observations 3,418 3,418 1,712 3,418

Baseline Controls X X X

High Urban X

Urban Share X

Avg. Change Black pop. 2,314 2,314 4,456 2,314

Avg. 1940 White pop. 71,001 71,001 120,557 71,001

Avg. Change White pop. 12,058 12,058 19,860 12,058

Notes: The sample is a panel of the 1,139 non-southern US counties (see Table A.1 for our definition of southernstates) for which electoral returns in Congressional elections are available for all Census years between 1940 and1970, and with at least one African American resident in 1940. The table estimates stacked first differenceregressions, reporting 2SLS and first stage results in Panels A and B respectively. The dependent variable is thedecadal change in the white population in the county. The main regressor of interest is the change in the Blackpopulation in the county, instrumented with the shift-share instrument described in equation (2) in the text. Allregressions control for state by period fixed effects. Columns 2 to 4 further include interactions between perioddummies and: i) the 1940 Black share; and ii) a dummy equal to 1 if the Democratic vote share in 1940 washigher than the Republicans vote share). Column 3 restricts attention to counties with 1940 urban share of thepopulation above the sample median (0.356). Column 4 replicates column 2 by including interactions betweenperiod dummies and the 1940 urban share of the population. F-stat is the KP F-stat for weak instruments.Robust standard errors, clustered at the county level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05,∗ p< 0.1.

45

Table 5. Whites’ Most Important Problem (ANES)

Dep. Variable 1[Pro Civil Rights: Most Important Problem]

(1) (2) (3) (4) (5) (6)

OLS OLS OLS OLS 2SLS 2SLS

Panel A. Main estimates1[Pro Segregation] -0.078∗∗∗

(0.023)

1[Against School -0.078∗∗∗

Integration] (0.019)

1[Against Housing/Work -0.077∗∗

Integration] (0.031)

Change Black Share 0.010 0.017∗∗ 0.022∗∗

(0.010) (0.008) (0.011)

Panel B. First StagePredicted Change 2.184∗∗∗ 2.270∗∗∗

Black Share (0.321) (0.340)

Geography FE State State State Region Region Region

State Controls X X X

Non-Movers X

F-stat 46.17 44.57

Observations 1,570 1,407 1,423 1,602 1,602 927

Mean Dep. Var. 0.105 0.105 0.105 0.105 0.105 0.110

Notes: The sample is restricted to white ANES respondents living in the US North in years 1960 and 1964. Thedependent variable is a dummy equal to 1 if the respondent reports that supporting civil rights is among themost important issues facing the country at the time of the interview (see online appendix D for exact wordingand additional details on the construction of the variable). The regressor of interest in column 2 (resp. column3) is a dummy equal to 1 if the respondent is against the integration of schools (resp. of working environmentand housing). Pro-segregation in column 1 is a dummy if the respondent is either against school integration oragainst working-housing integration. Change Black share (columns 4 to 6) is the change in the Black share atthe state level between 1940 and 1960. Column 4 reports OLS estimates, while columns 5 and 6 present 2SLSestimates, instrumenting the change in the Black share with the predicted number of Black migrants over 1940state population. All regressions include survey year fixed effects and individual controls of respondents (gender,age and education fixed effects and marital status). Columns 1 to 3 control for state fixed effects, while columns4 to 6 control for region fixed effects and 1940 state characteristics (Black share; Democratic incumbency inCongressional elections; share in manufacturing; share of workers in the CIO; urban share). Column 6 restrictsthe sample to respondents who were born in the same state where they lived at the time of the interview. Thebottom row reports the average of the dependent variable. F-stat in columns 5 and 6 is the K-P F-stat for weakinstrument. Panel B reports the first stage. Robust standard errors, clustered at the state level, in parentheses.∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

46

Table 6. Black in-Migration, Residential Segregation, and Local Governments

Dep. Variable Change in Change Log(Number of local governments)

1[Pro-civil Total School Special Municipalities

rights protest] districts districts

(1) (2) (3) (4) (5)

Panel A. Full sampleChange Black Share 0.047*** 0.052*** 0.103*** 0.000 0.008

(0.011) (0.010) (0.017) (0.023) (0.009)

KP F-stat 13.65 13.41 13.41 13.41 13.41

Observations 3,418 3,399 3,399 3,399 3,399

Panel B. Residential Segregation Below MedianChange Black Share -0.004 0.014 -0.050 -0.056 0.037

(0.017) (0.051) (0.086) (0.110) (0.026)

KP F-stat 14.66 14.66 14.66 14.66 14.66

Observations 1,445 1,445 1,445 1,445 1,445

Panel C. Residential Segregation Above MedianChange Black Share 0.053** 0.048*** 0.109*** -0.028 0.018

(0.023) (0.015) (0.025) (0.026) (0.012)

KP F-stat 7.524 7.555 7.555 7.555 7.555

Observations 1,443 1,428 1,428 1,428 1,428

Notes: Panel A estimates the baseline specification with 2SLS (with interactions between year dummies and: i) 1940 Black share;ii) 1940 Democratic incumbency dummy; iii) state fixed effects). Panel B (resp. C) estimates the same set of regressions for countieswith residential segregation below (resp. above) the sample median. Residential segregation refers to the index constructed inLogan and Parman (2017). The table reports 2SLS results replicating the baseline specification. All regressions are weighed by 1940population. Standard errors, clustered at the county level, in parentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

47

Table 7. Changes in Legislators’ Ideology

Dep. Variable Change in Civil Rights Ideology (Lower values = more liberal ideology)

Agnostic Scores Constrained Scores

(Baseline mean: -0.872) (Baseline mean: -0.853)

(1) (2) (3) (4) (5) (6)

Panel A. 2SLS

Change Black Share -0.055 -0.307** 0.047 -0.058 -0.345*** 0.059

(0.042) (0.121) (0.059) (0.043) (0.130) (0.063)

Panel B. First Stage

Predicted Change 1.472*** 1.006*** 1.809*** 1.455*** 1.002*** 1.783***

Black Share (0.441) (0.379) (0.555) (0.445) (0.378) (0.562)

F-stat 11.14 7.068 10.60 10.71 7.038 10.08

Observations 571 286 285 571 286 285

Congress Period 78-82; 82-88 78-82 82-88 78-82; 82-88 78-82 82-88

Notes: The dependent variable is the change in the civil rights ideology scores from Bateman et al. (2017) –“Agnostic” scores in columns 1 to 3, and “Constrained” scores in columns 4 to 6. Lower (resp. higher) valuesof the score refer to more liberal (resp. conservative) ideology (see also Bateman et al., 2017, for more details).Columns 1 and 4 (resp. 2-3, and 5-6) estimate stacked first difference regressions (resp. first difference regressionsfor Congress period 78-82 and 82-88). Panel A reports 2SLS results, while Panels B presents first stage estimates.All regressions are weighed by 1940 congressional district population and control for state by year fixed effectsand include interactions between year dummies and: i) the 1940 Black share in the congressional district; ii) adummy for Democratic incumbency in the 78th Congress in the district; and iii) the ideology score in the districtin the 78th Congress. First difference regressions do not include interactions with year dummies since these areautomatically dropped. F-stat refers to the K-P F-stat for weak instrument. Robust standard errors, clusteredat the congressional district level, in parentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

48

Table 8. Heterogeneous Effects on Legislators’ Ideology

Dep. Variable Change in civil rights ideology Change in ideology indicator of elected Congress members

Agnostic Constrained Liberal Moderate Moderate Conservative

Score Score Democrat Democrat Republican Republican

(1) (2) (3) (4) (5) (6)

Change Black Share -0.095* -0.096* 0.081** -0.078 -0.025 0.026

(0.050) (0.050) (0.039) (0.053) (0.031) (0.035)

Change Black -0.011 -0.014

Share*DR (0.033) (0.033)

Change Black 0.058** 0.056**

Share*RR (0.025) (0.025)

Change Black -0.072* -0.071*

Share*RD (0.038) (0.036)

F-stat (Main) 5.98 6.02 11.14 11.14 11.14 11.14

F-stat (DR) 7.13 7.02

F-stat (RR) 13.35 13.67

F-stat (RD) 14.68 16.14

Observations 567 567 571 571 571 571

Notes: The dependent variable is the change in the civil rights ideology score from Bateman et al. (2017) in columns 1 and 2,and the change in the ideology indicator of the Congress member in office in columns 3 to 6. The ideology indicators are definedas: i) liberal (resp. moderate) Democrat if the legislator’s score was below (resp. above) the median score of the Democratic Partymembers in the 78th Congress; ii) moderate (resp. conservative) Republican if the legislator’s score was below (resp. above) themedian score of the Republican Party members in the 78th Congress. Columns 1 and 2 present 2SLS results where the changein the Black share is interacted with dummies for a party transition during the Congress period. The omitted category is that ofdistricts remaining Democratic throughout the period. F-stats in columns 1 and 2 refer to the Angrist and Pischke F-stat for weakinstruments in each first stage, and to the K-P F-stat for weak instruments in columns 3 to 6. All regressions are weighed by 1940congressional district population and control for state by year fixed effects and include interactions between year dummies and: i)the 1940 Black share in the congressional district; ii) a dummy for Democratic incumbency in the 78th Congress in the district; andiii) the ideology score in the district in the 78th Congress. Robust standard errors, clustered at the congressional district level, inparentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

49

A Appendix – Additional Figures and Tables

Figure A.1. Black Emigration Rates from the South, by Decade

Notes: The figure plots the Black emigration rate from the US South for each decade.Source: Adapted from Boustan (2016).

Figure A.2. Northern Legislators Supporting Civil Rights Bills, by Party

Notes: Blue (resp. red) bars plot the share of Democrat (resp. Republican) membersof Congress in the non-South US voting in favor of bills in support of civil rightsbetween the 78th and the 88th Congresses. The first two bars refer to the averagebetween the 78-82 and the 83-88 periods, while the remaining bars display resultsfor each Congress period separately. The 9 bills on the civil rights voted upon inCongress between the 78th and the 88th Congress are listed in Table A.2.

50

Figure A.3. Overall Support for Civil Rights Bills, by Party

Notes: Blue (resp. red) bars plot the share of Democrat (resp. Republican) membersof US Congress voting in favor of bills in support of civil rights between the 78th andthe 88th Congresses. The first two bars refer to the average between the 78-82 andthe 83-88 periods, while the remaining bars display results for each Congress periodseparately. The 9 bills on the civil rights voted upon between the 78th and the 88th

Congress are listed in Table A.2.

Figure A.4. Discharge Petitions on Civil Rights Signed by Northern Legislators

Notes: Blue (resp. red) bars plot the share of Democrat (resp. Republican) membersof Congress in the non-South US signing discharge petitions in favor of civil rightsbills between the 78th and the 88th Congresses. The first two bars refer to theaverage between the 78-82 and the 83-88 periods, while the remaining bars displayresults for each of the two Congress periods separately.

51

Figure A.5. Black Share in 1940

Notes: The map plots the 1940 share of Blacks (divided by county population) forthe non-southern counties in our sample.

Figure A.6. Black Share in Northern Counties, 1940 vs 1970

Notes: Black share of the population for selected non-southern counties in 1940(light blue) and in 1970 (Black). Source: Authors’ calculation from IPUMS data.

52

Figure A.7. Share of Southern Born Blacks in Northern Counties, 1940

Notes: Share of African Americans born in selected southern states living in non-southern counties in 1940. Source: Authors’ calculation from IPUMS data.

Figure A.8. Frequency of CORE Demonstrations, by Type

Notes: The figure plots the number of CORE demonstrations as share of all events oc-curring in our sample period, for each of the four main categories described in the text.The portion of the bar filled with oblique lines (resp. dots) refers to the share of eventsof each category that involved local (resp. national) issues.

53

Figure A.9. Effects of Black in-Migration on CORE Demonstrations, by Type

Notes: The figure plots the 2SLS coefficient (with corresponding 95% confidence intervals)for the effects of the change in the Black share on the change in CORE demonstrations.The first dot from the left considers all demonstrations in our sample; the next four dotsrefer to each specific cause, reported on the x-axis;the last two dots on the right refer todemonstrations that involved, respectively, local and national issues. All regressions areweighed by 1940 population, and include interactions between year dummies and: i) statefixed effects; ii) the 1940 Black share; and iii) a dummy equal to 1 if the Democratic voteshare in 1940 was higher than the Republican vote share. The corresponding estimatesare reported in Table A.7.

54

Figure A.10. Scenarios on Black Behavior and Implied Number of White Switchers

Notes: The red markers indicate the number of votes for the DemocraticParty implied by the 2SLS point estimate in column 6 of Table 2 in a countywith average native-born population and average change in the share ofBlacks. Bars indicate votes cast for the Democratic Party by different seg-ments of the population under different assumptions about turnout and vot-ing preferences of Blacks. In each scenario, “whites” indicates the numberof votes cast for the Democratic Party if white voters did not change theirbehavior in response to Black inflows. Scenario 1 : turnout is 70% for north-ern voters, all Black migrants voted, 70% of northern Blacks and all Blackmigrants voted Democrats. Scenario 2 : Black turnout rate is 70%, 70% ofnorthern Black voters and all Black migrants voted for Democrats. Scenario3 : Black turnout rate is 70%, 70% of Blacks (both migrants and northernresidents) voted for Democrats. Scenario 4 : Black turnout rate is 50%, 70%of Blacks (both migrants and northern residents) voted for Democrats.

55

Figure A.11. Probability that Civil Rights is Most Important Issue for Whites

Notes: The figure plots 2SLS coefficients (with corresponding 95% confidence intervals) for aregression where the dependent variable is the “pro civil rights” dummy reported in Table 5.The first two bars refer to respondents who are not and who are union members (light anddark colors respectively); the third and fourth (resp. fifth and sixth) bars restrict attentionto individuals who are not and who are Democrats (resp. Republicans), with darker colorsreferring to members of the group.

56

Table A.1. List of Southern States

Alabama North Carolina

Arkansas Oklahoma

Florida South Carolina

Georgia Tennessee

Kentucky Texas

Louisiana Virginia

Mississippi West Virginia

Notes: The table presents the listof southern states considered inour analysis. We follow the Cen-sus definition except for Delawareand Maryland: as Boustan (2010)we assign these to the North, asthey were net recipient of Blackmigrants during this period.

Table A.2. Civil Rights Bills Voted in the House, 1943-1964

Congress Year Bill Number Northern Democrats Northern Republicans

Voting Yes Voting Yes

78 1943 HR-7 0.830 0.795

79 1945 HR-7 0.842 0.697

80 1947 HR-29 0.913 0.982

81 1949 HR-3199 0.942 0.696

81 1950 HR-4453 0.790 0.720

84 1956 HR-627 0.914 0.875

85 1957 HR-6127 0.927 0.843

86 1960 HR-8601 0.843 0.813

88 1964 HR-7152 0.918 0.817

Notes: The table lists the bills voted upon in the House of Representatives between Congress 78 and Congress88. The last two columns report the share of northern Democrats (resp. Republicans) who voted in favor ofeach bill relative to all northern Democrats (resp. Republicans).

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Table A.3. Discharge Petitions, by Party

Poll Tax Lynching FECP Housing Civil Rights Act Total

Panel A. Congress Period: 78th − 82nd

Share Democrats 0.564 0.552 0.500 0.138 - 0.422

Share Republicans 0.304 0.239 0.132 0.024 - 0.147

Panel B. Congress Period: 83rd − 88th

Share Democrats - - 0.632 - 0.677 0.651

Share Republicans - - 0.043 - 0.175 0.154

Notes: The table presents the share of Democrats and Republicans signing discharge petitions on each topic reported inthe top row for the 78-82 (resp. 83-88) Congresses in Panel A (resp. Panel B). When no discharge petition of a given typewas filed in a congress period, the corresponding entry is left missing. Table A.4 reports additional summary statistics forsignatures on discharge petitions. See Table A.5 for the complete list of discharge petitions (by date and by topic). Source:authors’ calculation from Pearson and Schickler (2009).

Table A.4. Discharge Petitions: Summary Statistics

Variables Mean Median St. Dev. Min Max Obs

Panel A: Discharge Petitions by Issue - Congress Period

Poll Tax Lynching FECP Housing Civil Rights Act Total

78th to 82nd 4 3 5 2 0 14

83rd to 88th 0 0 2 1 5 8

Panel B: Discharge Petitions by Legislator – Summary Statistics

Mean Median St. Dev. Min Max Obs.

78th to 82nd 0.772 0.600 0.553 0 2.333 298

83rd to 88th 0.441 0.385 0.298 0 1.286 298

Notes: Panel A presents the number of discharge petitions filed in the two Congress periods (78-82 and 83-88) by type. Panel B reports the summary statistics for the number of petitions signed per legislator for theCongressional Districts in our sample, in either Congress period.

58

Table A.5. Discharge Petitions by Type and Date

Congress Number Topic Total Signatures

73 14 House Restaurant Desegregation 145

74 32 Lynching 218

75 1 Lynching 75

75 5 Lynching 218

76 10 Lynching 218

76 12 Lynching 59

76 34 Poll Tax 49

77 1 Poll Tax 218

77 3 Lynching 59

77 4 Poll Tax 31

77 15 Lynching 29

78 1 Poll Tax 10

78 3 Poll Tax 219

78 5 Lynching 82

78 18 FEPC 41

79 1 Poll Tax 218

79 3 Lynching 150

79 4 FEPC 187

79 24 Public Accommodation 6

80 2 Poll Tax 41

80 9 Lynching 80

81 7 Housing Discrimination 24

81 20 FEPC 110

81 21 FEPC 100

82 6 FEPC 16

83 4 Public Accommodation 71

83 5 FEPC 72

84 5 Civil Rights Act 148

85 1 Civil Rights Act 105

85 6 Civil Rights Act 3

86 3 Civil Rights Act 214

88 2 Anti-Discrimination 4

88 5 Civil Rights Act 174

91 11 Fair Employment 136

Notes: The table reports the list of all pro-civil rights discharge petitions filed between Congresses 73 and 91.Source: adapted from Pearson and Schickler (2009).

59

Table A.6. Congressional Elections by Decade and Presidential Elections

Dep. Variable Congressional Elections Presidential Elections

(1) (2) (3) (4)

Panel A.Democrat Vote Share

Change Black Share 3.051** 0.461 2.374*** 0.577***

(1.513) (0.317) (0.608) (0.144)

Panel B. Turnout

Change Black Share 1.035 0.033 0.504 0.550**

(0.695) (0.337) (0.490) (0.215)

Panel C. First Stage

Predicted Change 0.787*** 1.175*** 1.412*** 1.148***

Black share (0.244) (0.305) (0.441) (0.311)

F-stat 10.41 14.84 10.27 13.21

Observations 1,139 1,139 1,140 3,416

Decade 1940s 1950s 1960s All

Avg. Change Black Share 1.384 1.994 2.331 1.907

Notes: The sample includes the 1,139 non-southern US counties (see Table A.1 for the definition of southernstates) for which electoral returns in Congressional elections are available for all Census years between 1940 and1970, and with at least one African American resident in 1940. The table replicates column 6 of Table 2 forCongressional elections separately for each decade in columns 1 to 3, and for Presidential elections in column4. The main regressor of interest is the change in the Black share, which is instrumented with the shift-shareinstrument described in equation (2) in the text. All regressions are weighed by 1940 county population, andinclude: i) state fixed effects; ii) the 1940 Black share; and iii) a dummy equal to 1 if the Democratic voteshare in 1940 was higher than the Republicans vote share. In column 4, these controls are interacted with yeardummies. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level,in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

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Table A.7. CORE Demonstrations, by Type

Dep. Variable Cause Relevance

All School/ Housing Police Access to Goods Local NationalCollege Brutality

(1) (2) (3) (4) (5) (6) (7)

Panel A. 2SLS

Change Black Share 0.047*** 0.025* 0.019 0.011 0.049*** 0.053*** 0.020*(0.011) (0.013) (0.012) (0.010) (0.012) (0.011) (0.012)

Panel B. First Stage

Predicted Change Black 1.148*** 1.148*** 1.148*** 1.148*** 1.148*** 1.148*** 1.148***Share (0.311) (0.311) (0.311) (0.311) (0.311) (0.311) (0.311)

F-stat 13.65 13.65 13.65 13.65 13.65 13.65 13.65Observations 3,418 3,418 3,418 3,418 3,418 3,418 3,418

Notes: The sample includes the 1,139 non-southern US counties (see Table A.1 for the definition of southern states)for which electoral returns in Congressional elections are available for all Census years between 1940 and 1970, andwith at least one African American resident in 1940. Panel A (resp. B) reports 2SLS (resp. first stage) estimates.The dependent variable is the decadal change in the probability of a protest in each category occurring over adecade. The main regressor of interest is the change in the Black share, which is instrumented with the shift-shareinstrument described in equation (2) in the text. All regressions are weighed by 1940 county population, and includeinteractions between year dummies and: i) state fixed effects; ii) the 1940 Black share; and iii) a dummy equal to 1if the Democratic vote share in 1940 was higher than the Republican vote share. F-stat is the K-P F-stat for weakinstruments. Robust standard errors, clustered at the county level, in parenthesis. Significance levels: ∗∗∗ p< 0.01,∗∗ p< 0.05, ∗ p< 0.1.

61

Table A.8. NAACP Chapters

Dep. Variable 1940-1960 Change in 1[NAACP Chapter]

(1) (2) (3) (4)

OLS OLS 2SLS 2SLS

Panel A. Main Estimates

Change Black Share -0.023∗∗∗ 0.048∗∗∗ -0.028 0.075∗∗

(0.008) (0.17) (0.024) (0.035)

Panel B. First Stage

Predicted Change Black 0.761∗∗∗ 0.609∗∗

Share (0.228) (0.240)

NAACP in 1940 YES NO YES NO

F-stat 11.18 6.438

Observations 1,139 932 1,139 932

Notes: The sample includes the 1,139 non-southern US counties (see Table A.1 for the definition of southernstates) for which electoral returns in Congressional elections are available for all Census years between 1940and 1970, and with at least one African American resident in 1940. The dependent variable is the change(between 1940 and 1960) in the presence of NAACP chapters. Columns 2 and 4 restrict attention to countieswith no NAACP chapter in 1940. Columns 1 and 2 estimate OLS regressions, whereas columns 3 and 4 present2SLS results. The main regressor of interest is the 1940-1960 Change Black Share, and is instrumented withthe shift-share instrument constructed in the text in columns 3 and 4. All regressions are weighed by 1940county population, and include interactions between year dummies and: i) state fixed effects; ii) the 1940 Blackshare; and iii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republican voteshare. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, inparenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

62

Table A.9. Congressional Elections (CZ)

(1) (2) (3) (4) (5) (6) (7)OLS OLS OLS 2SLS 2SLS 2SLS 2SLS

Panel A. Democrat Vote Share

Change Black Share 0.672** 0.648 0.821** 0.945** 1.886** 2.759*** 3.076***(0.287) (0.416) (0.393) (0.470) (0.952) (1.052) (1.101)

Panel B. Turnout

Change Black Share -0.453*** -0.346* -0.287 -0.452*** 0.361 0.673 0.754(0.132) (0.177) (0.183) (0.171) (0.442) (0.569) (0.633)

Panel C. First Stage

Predicted Change Black 1.033*** 0.755*** 0.671*** 0.679***Share (0.094) (0.176) (0.171) (0.181)

Specification FD FD FD FD FD FD LD1940 Black Share X X X X X1940 Dem Incumbent X X X

F-stat 119.5 18.39 15.43 14.16Observations 1,125 1,125 1,125 1,125 1,125 1,125 375

Notes: The table replicates Table 2 by aggregating the unit of analysis to the commuting zone (CZ). Thedependent variable is the change in the Democratic vote share in Congressional elections (resp. turnout) in PanelA (resp. in Panel B). Panel C reports first stage coefficients. Columns 1 to 3 estimate OLS regressions, whileremaining columns report 2SLS estimates. The main regressor of interest is the change in the Black share, whichis instrumented with the shift-share instrument described in equation (2) in the text from column 4 onwards.All regressions are weighed by 1940 CZ population, and control for state by period fixed effects. 1940 Blackshare (resp. 1940 Dem dummy) refers to interactions between period dummies and the 1940 Black share (resp. adummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share). F-stat is theK-P F-stat for weak instruments. Robust standard errors, clustered at the CZ level, in parenthesis. Significancelevels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

63

Table A.10. CORE Demonstrations (CZ)

Dep. Variable Change in Pr.(Pro-civil rights demonstration)

(1) (2) (3) (4) (5) (6) (7)

Panel A. 2SLS

Change Black Share 0.053*** -0.003 -0.002 0.096*** 0.046 0.042 0.042(0.016) (0.023) (0.023) (0.014) (0.031) (0.036) (0.030)

Panel B. First Stage

Predicted Change Black 1.033*** 0.755*** 0.671*** 0.671***Share (0.094) (0.176) (0.171) (0.171)

1940 Black Share X X X X X1940 Dem Incumbent X X XWhite participants X

F-stat 119.5 18.39 15.43 15.43Observations 1,125 1,125 1,125 1,125 1,125 1,125 1,125

Notes: This table replicates Table 3 by aggregating the unit of analysis to the commuting zone (CZ).The dependent variable is the change in the probability of non-violent demonstrations in support of civilrights coordinated by the CORE. Columns 1 to 3 estimate equation (1) in the text with OLS, whileremaining columns report 2SLS estimates. The main regressor of interest is the change in the Blackshare, which is instrumented with the shift-share instrument described in equation (2) in the text fromcolumn 4 onwards. All regressions are weighed by 1940 CZ population, and control for state by periodfixed effects. 1940 Black share (resp 1940 Dem dummy) refers to interactions between period dummiesand the 1940 Black share (resp. a dummy equal to 1 if the Democratic vote share in 1940 was higherthan the Republicans vote share). Column 7 includes only those demonstrations that were joined by atleast some white participants. F-stat is the K-P F-stat for weak instruments. Robust standard errors,clustered at the CZ level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

64

Table A.11. Black in-Migration and White Population (Long Differences)

Dep. Variable Change White population

(1) (2) (3) (4)

Panel A. 2SLS

Change Black population 0.471 0.441 0.384 0.320

(0.412) (0.405) (0.383) (0.413)

Panel B. First Stage

Predicted Change Black population 2.504∗∗∗ 2.516∗∗∗ 2.526∗∗∗ 2.474∗∗∗

(0.365) (0.353) (0.339) (0.352)

F-stat 46.96 50.69 55.46 49.53

Observations 1,139 1,139 569 1,139

Baseline controls X X X

High urban X

Urban share X

Avg. change Black pop. 2,314 2,314 4,456 2,314

Avg. 1940 White pop. 71,001 71,001 120,557 71,001

Avg. change White pop. 12,058 12,058 19,860 12,058

Notes: The sample includes a panel of the 1,139 non-southern US counties (see Table A.1 for our definition ofsouthern states) for which electoral returns in Congressional elections are available for all Census years between1940 and 1970, and with at least one African American resident in 1940. The table estimates long differenceregressions, reporting 2SLS and first stage results in Panels A and B respectively. The dependent variable isthe 1940-1970 change in the white population in the county. The main regressor of interest is the correspondingchange in the Black population, instrumented with the shift-share instrument described in equation (2) in thetext. All regressions control for state fixed effects. Columns 2 to 4 further include i) the 1940 Black share;and ii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share).Column 3 restricts attention to counties with 1940 urban share of the population above the sample median(.356). Column 4 replicates column 2 by including the 1940 urban share of the population. F-stat is the KPF-stat for weak instruments. Robust standard errors, clustered at the county level, in parenthesis. Significancelevels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

65

Table A.12. Black in-Migration and white flight

Dep. variable: Change White Population Change White Populationin the county in central cities

(1) (2) (3) (4)

Panel A. 2SLS

Change Black population 0.155 0.236 -1.783*** -1.713***

(0.399) (0.347) (0.299) (0.293)

Panel B. First Stage

Predicted Change Black 2.784*** 2.701*** 1.722*** 1.876***

population (0.274) (0.266) (0.134) (0.203)

F-stat 102.9 103.1 166.3 85.40

Observations 351 351 154 154

Baseline Controls X X

Geography County County MSA MSA

Avg. Change Black pop. 18,661 18,661 42,919 42,919

Avg. 1940 White pop. 361,119 361,119 575,513 575,513

Avg. Change White pop. 59,198 59,198 -21,488 -21,488

Notes: In columns 1 and 2, the sample includes a panel of the 117 non-southern US counties contained in the 52metropolitan statistical areas (MSAs) included in Boustan (2010), for which electoral returns in Congressionalelections are available for all Census years between 1940 and 1970, and with at least one African Americanresident in 1940. Columns 3-4 focus on the 52 central cities contained in the 52 MSAs included in Boustan(2010). The dependent variable is the decadal change in the white population in the county (resp. in the centralcity) in columns 1-2 (resp. 3-4). The main regressor of interest is the change in the Black population in thecounty (resp. in the central city) in columns 1-2 (resp. 3-4), instrumented with the shift-share instrumentdescribed in equation (2) in the text. The table estimates stacked first difference regressions, reporting 2SLSand first stage results in Panels A and B, respectively. All regressions control for state by period fixed effects,and include interactions between period dummies and: i) the 1940 Black share; and ii) a dummy equal to 1if the Democratic vote share in 1940 was higher than the Republicans vote share. F-stat is the KP F-stat forweak instruments. Robust standard errors, clustered at the county level, in parenthesis. Significance levels: ∗∗∗

p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

66

Table A.13. Black in-Migration and white flight (Long Differences)

Dep. variable: Change White Population Change White Populationin the county in central cities

(1) (2) (3) (4)

Panel A. 2SLS

Change Black population 0.313 0.365 -1.765*** -1.659***

(0.373) (0.320) (0.308) (0.278)

Panel B. First Stage

Change Predicted Black 2.872*** 2.762*** 5.249*** 5.596***

population (0.304) (0.300) (0.555) (0.584)

KP F-stat 71.52 66.51 102.7 89.23

Observations 115 115 52 52

Baseline Controls X X

Geography County County MSA MSA

Avg. Change Black pop. 18,661 18,661 42,919 42,919

Avg. 1940 White pop. 361,119 361,119 575,513 575,513

Avg. Change White pop. 59,198 59,198 -21,488 -21,488

Notes: In columns 1 and 2, the sample includes a panel of the 117 non-southern US counties contained in the 52metropolitan statistical areas (MSAs) included in Boustan (2010), for which electoral returns in Congressionalelections are available for all Census years between 1940 and 1970, and with at least one African Americanresident in 1940. Columns 3-4 focus on the 52 central cities contained in the 52 MSAs included in Boustan(2010). The dependent variable is the decadal change in the white population in the county (resp. in the centralcity) in columns 1-2 (resp. 3-4). The main regressor of interest is the change in the Black population in thecounty (resp. in the central city) in columns 1-2 (resp. 3-4), instrumented with the shift-share instrumentdescribed in equation (2) in the text. The table estimates long difference regressions, reporting 2SLS and firststage results in Panels A, and B, respectively. All regressions control for state fixed effects, and include: i)the 1940 Black share; and ii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than theRepublicans vote share. F-stat is the KP F-stat for weak instruments. Robust standard errors, clustered at thecounty level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

67

Table A.14. Congressional Elections (CZ), Restricted Sample

Dep. Variable Change in

Democratic Vote Share Turnout

(1) (2) (3) (4)

Panel A. 2SLS

Change Black Share 2.759*** 2.704*** 0.673 0.942

(1.052) (0.920) (0.569) (0.617)

Panel B. First Stage

Predicted Change 0.671*** 0.763*** 0.671*** 0.763***

Black Share (0.171) (0.173) (0.171) (0.173)

Sample Baseline Restricted Baseline Restricted

(1960 Census) (1960 Census)

F-stat 15.43 19.44 15.43 19.44

Observations 1,125 375 1,125 375

Notes: The table replicates the CZ level results reported in Table A.9 by restricting the sample to CZs forwhich 1960 US Census data are available in columns 2 and 4 (columns 1 and 3 report results in column 6 ofTable A.9). The dependent variable is the change in the Democratic vote share in Congressional elections (resp.turnout) in columns 1-2 (resp. in columns 3-4). Panel B reports first stage coefficients. The main regressor ofinterest is the change in the Black share, which is instrumented with the shift-share instrument described inequation (2) in the text from column 4 onwards. All regressions are weighed by 1940 CZ population, and controlfor interactions between year dummies and: i) state dummies; ii) the 1940 Black share; and, iii) a dummy equalto 1 if the Democratic vote share in 1940 was higher than the Republicans vote share. F-stat is the K-P F-statfor weak instruments. Robust standard errors, clustered at the county level, in parenthesis. Significance levels:∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

68

Table A.15. Black in-Migration and Changes in Whites’ Characteristics

(1) (2) (3) (4) (5)

High Skilled In Manufacture In Labor Force Homeowner 65+

Panel A. 2SLS

Change Black Share -0.348 1.229 -0.389 -0.021 0.108

(0.626) (0.764) (0.424) (0.526) (0.334)

Panel B. First Stage

Predicted Change Black 0.922∗∗∗ 0.922∗∗∗ 0.922∗∗∗ 0.922∗∗∗ 0.922∗∗∗

Share (0.179) (0.179) (0.179) (0.179) (0.179)

F-stat 26.39 26.39 26.39 26.39 26.39

Observations 125 125 125 125 125

1940 Mean 13.33 20.72 85.76 50.27 10.21

Avg. Change Black Share 3.372 3.372 3.372 3.372 3.372

Notes: The dependent variable is the 1940-1960 change in the share of white men above 18 not enrolled inschool who are: i) high skilled (column 1); ii) employed in manufacturing (column 2); iii) in the labor force(column 3); iv) homeowner (column 4); v) above the age of 65 (column 5). The table reports 2SLS results forthe 1940-1960 change in the Black share, instrumented with the shift-share IV described in the main text. Theanalysis is restricted to the 125 CZs for which demographic variables were available from the 1960 5% sampleof the micro-census. All regressions are weighed by 1940 population, control for state fixed effects, and includei) the 1940 Black share, and ii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than theRepublicans vote share. F-stat is the KP F-stat for weak instruments. Robust standard errors, clustered at theCZ level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

69

Table A.16. Black Inflows and Whites’ Economic Outcomes

Dep. Variable 1940-1960 Change in

Labor Employed Manufacturing Log Occupational Log

Force Scores Wages

(1) (2) (3) (4) (5)

Panel A. 2SLS

Change Black Share -0.389 -0.512 1.229 0.003 0.077

(0.424) (0.488) (0.764) (0.007) (0.068)

Panel B. First Stage

Predicted Change 0.922∗∗∗ 0.922∗∗∗ 0.922∗∗∗ 0.922∗∗∗ 0.922∗∗∗

Black Share (0.179) (0.179) (0.179) (0.179) (0.179)

F-stat 26.39 26.39 26.39 26.39 26.39

Observations 125 125 125 125 125

1940 Mean Dep. Var. 85.82 78.68 16.64 3.037 5.755

Notes: In columns 1 to 3, the dependent variable is the 1940-1960 change in the share of white men above18 not enrolled in school who are: i) in the labor force (column 1); ii) employed (column 2); iii) employedin manufacturing (column 3). In columns 4 and 5, the dependent variable is the 1940-1960 change in the logoccupational score and in log wages for white men above 18 not enrolled in school. The table reports 2SLSresults for the effects of the 1940-1960 change in the Black share, instrumented with the shift-share IV describedin the main text. The analysis is restricted to the 125 CZs for which demographic variables were availablefrom the 1960 5% sample of the micro-census. All regressions are weighted by 1940 population, and control forstate fixed effects, and include: the 1940 Black share, and the 1940 Democratic winner dummy. The bottomrow reports the 1940 mean of the dependent variable. F-stat is the K-P F-stat for weak instruments. Robuststandard errors, clustered at the CZ level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

70

Table A.17. Black Inflows and Whites’ Economic Outcomes: Unskilled and Manufacturing

Dep. Variable 1940-1960 Change in

Share Employed Log Wages Share Employed Log wages

(1) (2) (3) (4)

Panel A. 2SLS

Change Black Share -0.003 -0.004 -0.002 0.006

(0.004) (0.027) (0.003) (0.024)

Panel B. First Stage

Predicted Change 0.922∗∗∗ 0.922∗∗∗ 0.922∗∗∗ 0.922∗∗∗

Black Share (0.179) (0.179) (0.179) (0.179)

F-stat 26.39 26.39 26.39 26.39

Observations 125 125 125 125

1940 Mean Dep. Var. 86.68 6.134 88.76 6.428

Sector Unskilled Unskilled Manufacturing Manufacturing

Notes: The dependent variable is the 1940 to 1960 change in the probability of employment and in log wages forwhite men not enrolled in school and above the age of 18 working in the unskilled sector (resp. in manufacturing)in columns 1 and 2 (resp. columns 3 and 4). The table reports 2SLS results for the effects of the 1940-1960change in the Black share, instrumented with the shift-share IV described in the main text. The analysis isrestricted to the 125 CZs for which demographic variables were available from the 1960 5% sample of the micro-census. All regressions are weighted by 1940 population, and control for state fixed effects, and include: the 1940Black share, and the 1940 Democratic winner dummy. The bottom row reports the 1940 mean of the dependentvariable. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the CZ level, inparenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

71

Table A.18. Placebo: Black in-Migration and White Movers (ANES)

Dep. Variable 1[Mover]

(1) (2) (3) (4)

Panel A. 2SLS

Change Black share -0.003 0.002 -0.023 -0.001

(0.018) (0.022) (0.035) (0.020)

Panel B. First Stage

Predicted change Black share 2.305∗∗∗ 2.180∗∗∗ 2.280∗∗∗ 2.262∗∗∗

(0.351) (0.321) (0.345) (0.336)

F-stat 43.20 46.22 43.57 45.20

Observations 3,911 1,598 1,562 1,307

Sample All MIP sample Democrats Republicans

Mean dep. variable 0.429 0.436 0.446 0.403

Notes: The sample is restricted to white ANES respondents living in the US North during the survey waves1956 to 1964. The dependent variable is a dummy equal to 1 if the individual is living in a state different fromhis/her state of birth. Column 2 restricts attention to respondents in the sample of Table 5, while columns 3and 4 consider self-identified Democrats and Republicans respectively. All regressions are weighed with ANESsurvey weights, include region fixed effects, and control for individual characteristics of respondents (gender, ageand education fixed effects, and marital status) as well as for 1940 state characteristics (Black share; Democraticincumbency in Congressional elections; share in manufacturing; share of workers in the CIO; urban share).F-stat refers to the K-P F-stat for weak instrument. Panel B reports the first stage. Robust standard errors,clustered at the state level, in parentheses. ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

72

Table A.19. Whites’ Voting Behavior and Party Identification (ANES)

Dep. Variable 1[Vote Democratic] 1[Identify Democratic]

(1) (2) (3) (4) (5) (6)

Panel A. 2SLS

Change Black Share 0.029∗∗∗ 0.038∗∗∗ 0.056∗∗ 0.028∗∗∗ 0.030∗ 0.048∗∗∗

(0.006) (0.007) (0.028) (0.007) (0.016) (0.011)

Panel B. First Stage

Predicted Change 2.317∗∗∗ 2.383∗∗∗ 2.041∗∗∗ 2.282∗∗∗ 2.382∗∗∗ 2.047∗∗∗

Black Share (0.374) (0.388) (0.278) (0.358) (0.378) (0.277)

F-stat 38.47 37.81 54.01 40.56 39.62 54.47

Observations 3,207 1,648 695 4,699 2,296 1,035

Sample All Non-movers 1964 All Non-movers 1964

Mean dep. variable .488 .489 .593 .412 .398 .441

Notes: The sample is restricted to white ANES respondents living in the US North for ANES survey waves1956 to 1964. In columns 1 to 3 the dependent variable is a dummy equal to 1 if the respondent voted (resp.intended to vote) for the Democratic Party in the previous (resp. upcoming) Congressional election. In columns4 to 6 the dependent variable is a dummy equal to 1 if the respondent identified with the Democratic Party.All regressions are weighed with ANES survey weights, include survey year and region fixed effects, and controlfor individual characteristics of respondents (gender, age and education fixed effects, and marital status) aswell as for 1940 state characteristics (Black share; Democratic incumbency in Congressional elections; share inmanufacturing; share of workers in the CIO; urban share). Columns 2 and 5 restrict the sample to respondentswho did not migrate across states during their life (as of the year of the interview). Columns 3 and 6 focus onsurvey wave 1964. F-stat refers to the K-P F-stat for weak instrument. Panel B reports the first stage. Robuststandard errors, clustered at the state level, in parentheses. ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

73

Table A.20. Additional Evidence on Whites’ Attitudes: Gallup

Dep. Variable 1[Object to half 1[Vote for 1[Approve Civil 1[Racial integration at the

pupils in school] Black candidate] Rights Act] right pace/not fast enough]

(1) (2) (3) (4)

Panel A. 2SLS

Change Black share -0.051*** 0.088*** 0.045*** 0.008

(0.019) (0.025) (0.013) (0.012)

Panel B. First Stage

Predicted change Black share 2.025*** 2.191*** 2.177*** 2.248***

(0.209) (0.352) (0.322) (0.302)

F-stat 93.71 38.79 45.67 55.30

Observations 851 2,073 931 17,478

Mean dep. variable 0.289 0.525 0.705 0.319

Notes: The sample is restricted to white Gallup respondents living in the US North and to years 1963-1964. The dependentvariable is a dummy equal to 1 if respondent: i) objects to having half of the classroom composed of Black pupils (column1); ii) would vote for a Black candidate, were her party nominating one (column 2); iii) approves the Civil Rights Actintroduced in 1964 (column 3); and iv) thinks that the process of racial integration is occurring at the right pace or not fastenough (column 4). All regressions include region and survey year fixed effects, and control for individual characteristics ofrespondents (gender and age and education fixed effects) as well as for 1940 state characteristics (Black share; Democraticincumbency in Congressional elections; share in manufacturing; share of workers in the CIO; urban share). F-stat refers tothe K-P F-stat for weak instrument. Panel B reports the first stage. Robust standard errors, clustered at the state level, inparentheses. ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

74

Table A.21. Heterogeneity by County Characteristics - Social and Cultural forces

Dep. Variable Change Pr.(Civil Rights Demonstration)

(1) (2) (3) (4) (5)

Panel A. Socially progressive counties

Change Black Share 0.240** 0.088*** 0.097** 0.048 0.148*

(0.106) (0.033) (0.043) (0.030) (0.083)

F-stat 6.48 4.43 4.29 4.05 2.70

Observations 1,683 2,089 1,709 1,699 1,701

Panel B. Socially conservative counties

Change Black Share 0.039** 0.025** 0.011 -0.017 0.005

(0.016) (0.010) (0.029) (0.020) (0.024)

F-stat 10.42 8.634 7.531 4.926 13.68

Observations 1,680 1,329 1,709 1,703 1,701

Proxy Discrimination Miscegenation Immigrant Young Southern

index laws share Whites born Whites

Notes: Socially progressive (resp. conservative) counties are defined as those with: the discrimination index from Qian andTabellini (2020) below (resp. above) the median in column 1, miscegenation laws not in place (resp. in place) in column 2,immigrant share of the population above (resp. below) the median in columns 3, share of the white population of age 35 orless above (resp. below) the median in column 4, share of whites born in a southern state below (resp. above) the medianin column 5. The table reports 2SLS results replicating the baseline specification (with interactions between year dummiesand: i) 1940 Black share; ii) 1940 Democratic incumbency dummy; iii) state fixed effects. All regressions are weighed by1940 population. Standard errors, clustered at the county level, in parentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05,∗ p< 0.1.

75

Table A.22. Heterogeneity by County Characteristics - Economic and political forces

Dep. Variable Change Pr.(Civil Rights Demonstration)

(1) (2) (3) (4) (5)

Panel A. Above median

Change Black Share 0.071*** 0.063*** 0.070*** 0.057*** 0.095**

(0.012) (0.024) (0.026) (0.015) (0.039)

F-stat 24.49 9.97 8.67 18.33 15.99

Observations 1,720 1,699 1,700 1,703 1,695

Panel B. Below median

Change Black Share -0.009 -0.025 -0.022 0.038*** 0.004

(0.040) (0.021) (0.026) (0.013) (0.039)

F-stat 3.66 6.89 7.61 6.74 11.75

Observations 1,694 1,703 1,702 1,715 1,696

1940 Characteristic Political Share in Share Share CIO Predicted

competition manufacturinge unskilled workers economic growth

Notes: Political competition in column 1 is defined as (one minus) the absolute value of the margin of victoryin 1940 Congressional elections. Predicted economic growth in column 6 is constructed by interacting the 1940industry shares in a county with the national growth in that industry over each of the subsequent three decades(and then summed across industries). The employment share in manufacturing and the share of unskilled workersrefer to white men in working age (15-64). The table reports 2SLS results replicating the baseline specification(with interactions between year dummies and: i) 1940 Black share; ii) 1940 Democratic incumbency dummy;iii) state fixed effects. All regressions are weighed by 1940 population. Standard errors, clustered at the countylevel, in parentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

76

Table A.23. Probability that Civil Rights is Most Important Issue for Whites

Dep. Variable 1[Pro civil rights: Most important problem]

(1) (2) (3) (4) (5) (6)

Panel A. 2SLS

Change Black 0.107*** 0.002 0.032** -0.000 -0.020 0.025**

Share (0.020) (0.007) (0.015) (0.010) (0.013) (0.011)

Panel B. First Stage

Predicted Change 2.138*** 2.186*** 2.247*** 2.148*** 2.149*** 2.235***

Black Share (0.319) (0.316) (0.337) (0.309) (0.297) (0.334)

F-stat 44.95 47.95 44.36 48.41 52.28 44.66

Observations 465 1,133 670 930 517 1,083

Sample Union Non-Union Identified Identified Identified Identified

Members Members Democrat Non-Democrat Republican Non-Republican

Mean dep. variable 0.53 0.53 0.49 0.52 0.54 0.48

Notes: The sample is restricted to white ANES respondents living in the US North in years 1960 and 1964. The dependentvariable is a dummy equal to 1 if the respondent reports that supporting civil rights is among the most important issues facingthe country at the time of the interview (see online appendix D for exact wording and additional details on the constructionof the variable). The regressor of interest is the change in the Black share at the state level between 1940 and 1960, whichis instrumented with the predicted number of Black migrants over 1940 state population. All regressions include surveyyear and region fixed effects and individual controls of respondents (gender, age and education fixed effects and maritalstatus), and control for 1940 state characteristics (Black share; Democratic incumbency in Congressional elections; share inmanufacturing; share of workers in the CIO; urban share). Each column restricts attention to white respondents who belongto the group reported at the bottom of the table. F-stat is the K-P F-stat for weak instrument. Panel B reports the firststage. Robust standard errors, clustered at the state level, in parentheses. ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

77

Table A.24. Whites’ Feeling Thermometers

Dep. Variable Feeling Thermometer Towards

(1) (2) (3) (4)

Democrats Unions Blacks NAACP

Panel A. 2SLS

Change Black Share 2.646*** 2.924*** 0.843 1.329

(0.863) (0.855) (0.944) (0.903)

Panel B. First Stage

Predicted Change 2.045*** 2.041*** 2.041*** 2.031***

Black Share (0.276) (0.276) (0.276) (0.263)

F-stat 54.88 54.59 54.87 59.78

Observations 1,011 1,012 1,010 830

Mean Dep. Var. 68.70 57.64 60.82 53.55

Notes: : The sample is restricted to white ANES respondents living in the US North in 1964. The dependentvariable is the feeling thermometer towards each group at the top of the corresponding column. Higher valuesof the thermometer refer to warmer feelings. All regressions are weighed with ANES survey weights, includeregion fixed effects, and control for individual characteristics of respondents (gender, age and education fixedeffects, and marital status) as well as for 1940 state characteristics (Black share; Democratic incumbency inCongressional elections; share in manufacturing; share of workers in the CIO; urban share). F-stat refers to theK-P F-stat for weak instrument. Panel B reports the first stage. Robust standard errors, clustered at the statelevel, in parentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

78

Table A.25. Change in Signatures on Discharge Petitions

Dep. Variable Change in Signatures on Discharge

Petitions per Legislator (1940s)

Total FEPC Anti-Lynching Poll-Tax

(1) (2) (3) (4)

Panel A. 2SLS

Change Black Share 1.011** 0.648** 0.197 0.168*

(0.417) (0.294) (0.133) (0.101)

Panel B. First stage

Predicted Change 0.940*** 0.940*** 0.940*** 0.940***

Black Share (0.350) (0.349) (0.350) (0.350)

F-stat 7.218 7.243 7.218 7.218

Observations 294 293 294 294

Baseline mean 1.742 0.357 0.338 1.046

dep. variable

Notes: The dependent variable is the change in number of signatures on discharge petition per legislatorbetween the beginning and the end of the 1940 decade (see the main text for more details). Column 1 considersall discharge petitions, while columns 2 to 4 focus on employment protection legislation (FEPC), Anti-Lynchinglegislation, and Poll Tax discharge petitions respectively. Data on discharge petitions were kindly shared byKathryn Pearson and Eric Schickler (see also Pearson and Schickler, 2009). Panel A reports 2SLS results, whilePanel B presents first stage estimates. All regressions are weighed by 1940 congressional district population,include state fixed effects, and control for: i) the 1940 Black share in the congressional district; ii) a dummy forDemocratic incumbency in the 78th Congress in the district; and iii) the ideology score in the district in the78th Congress. F-stat refers to the K-P F-stat for weak instrument. Robust standard errors, clustered at thecongressional district level, in parentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

79

Table A.26. Change in Signatures on Discharge Petitions, Restricted

Dep. Variable Change in Signatures on Discharge

Petitions per Legislator (1940s)

Total FEPC Anti-Lynching Poll-Tax

(1) (2) (3) (4)

Panel A. 2SLS

Change Black Share 1.181*** 0.695** 0.240* 0.192*

(0.453) (0.318) (0.132) (0.109)

Panel B. First stage

Predicted Change 1.006*** 1.006*** 1.006*** 1.006***

Black Share (0.379) (0.378) (0.379) (0.379)

F-stat 7.068 7.093 7.068 7.068

Observations 286 285 286 286

Baseline mean 1.747 0.362 0.338 1.047

dep. variable

Notes: The dependent variable is the change in number of signatures on discharge petition per legislator betweenthe beginning and the end of the 1940 decade (see the main text for more details). The sample is restrictedto congressional districts for which the change in the ideology score between the 78th and the 82nd Congressis available. Column 1 considers all discharge petitions, while columns 2 to 4 focus on EPL, Anti-Lynchinglegislation, and Poll Tax discharge petitions respectively. Data on discharge petitions were kindly shared byKathryn Pearson and Eric Schickler (see also Pearson and Schickler, 2009). Panel A reports 2SLS results, whilePanel B presents first stage estimates. All regressions are weighed by 1940 congressional district population,include state fixed effects, and control for: i) the 1940 Black share in the congressional district; ii) a dummy forDemocratic incumbency in the 78th Congress in the district; and iii) the ideology score in the district in the78th Congress. F-stat refers to the K-P F-stat for weak instrument. Robust standard errors, clustered at thecongressional district level, in parentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

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Table A.27. Discharge Petitions (Levels on Changes)

Dep. Variable Number of petitions Share of petitions signed

per Legislator before 50th signature

(1) (2) (3) (4) (5) (6)

Panel A. 2SLS

Change Black Share 0.216* 0.412 0.138* 0.030** 0.053* 0.020*

(0.123) (0.269) (0.078) (0.013) (0.028) (0.012)

Panel B. First stage

Predicted Change 1.357*** 0.940*** 1.649*** 1.357*** 0.940*** 1.649***

Black Share (0.405) (0.326) (0.471) (0.405) (0.326) (0.471)

F-stat 11.24 7.218 10.69 11.24 7.218 10.69

Observations 588 294 294 588 294 294

Congress Period 78-82; 83-88 78-82 83-88 78-82; 83-88 78-82 83-88

Notes: The sample includes the 298 non-southern Congressional Districts that were representing non-southern US counties (seeTable A.1 for our definition of southern states) for which electoral returns in Congressional elections are available for all Censusyears between 1940 and 1970, with at least one African American resident in 1940, and for which data on signatures for dischargepetitions (Pearson and Schickler, 2009) were available. In columns 1-3, the dependent variable is the total number of signatureson discharge petitions per legislators during Congresses. In columns 4-6, the dependent variable is the share of discharge petitionssigned before the 50th signature. Columns 1 and 4 refer to both Congress periods 78-82 and 83-88, columns 2 and 5 to Congresses78-82 and columns 3 and 6 to Congresses 83-88. The main regressor of interest is the decadal change in the Black share in theCongressional District instrumented with the shift-share instrument described in the text. All regressions are weighed by 1940congressional district population, include state fixed effects, and control for: i) the 1940 Black share in the congressional district;ii) a dummy for Democratic incumbency in the 78th Congress in the district; and iii) the ideology score in the district in the 78thCongress. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the Congressional District level, inparenthesis Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

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Table A.28. Black in-Migration and Political Polarization

Dep. Variable Change in ideology indicator of

elected Congress members

Liberal Moderate Moderate Conservative

Democrat Democrat Republican Republican

(1) (2) (3) (4)

Panel A. 78-82 Congresses

Change Black Share 0.331** -0.306* 0.165 -0.177**

(0.135) (0.181) (0.136) (0.082)

F-stat 7.068 7.068 7.068 7.068

Observations 286 286 286 286

Panel B. 82-88 Congresses

Change Black Share -0.020 0.013 -0.101* 0.108**

(0.042) (0.056) (0.057) (0.046)

F-stat 10.60 10.60 10.60 10.60

Observations 285 285 285 285

Notes: The dependent variable is the change in the ideology indicator of the Congress member in office. Theideology indicators are defined as: i) liberal (resp. moderate) Democrat if the legislator’s score was below(resp. above) the median score of the Democratic Party members in the 78th Congress; ii) moderate (resp.conservative) Republican if the legislator’s score was below (resp. above) the median score of the RepublicanParty members in the 78th Congress. Panel A refers to Congress period 78-82; Panel B refers to Congressperiod 82-88. All regressions are weighed by 1940 congressional district population and control for state by yearfixed effects and include interactions between year dummies and: i) the 1940 Black share in the congressionaldistrict; ii) a dummy for Democratic incumbency in the 78th Congress in the district; and iii) the ideology scorein the district in the 78th Congress. K-P F-stat for weak instruments. Robust standard errors, clustered at thecongressional district level, in parentheses. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

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B Appendix – Matching Counties to Time-Invariant Congres-sional Districts

When studying the effects of Black inflows on the behavior of northern legislators, we face two

main difficulties. First, while the African American population and other demographic variables

are measured at the county level, legislators’ behavior is available at the CD level. Second, the

boundaries of CDs change over time due to redistricting. We overcome both challenges by first

matching counties to CDs, and then by constructing a time-invariant cross-walk to map CDs

that get redistricted over time to their baseline geography.

B.1 County-CD Crosswalk

To overcome the first problem, and to assign to each CD the corresponding “Black in-migration

shock” we perform a spatial merge of 1940 county maps with CDs, following the procedure

used in Feigenbaum and Hall (2015).50 Since there is no one-to-one mapping between counties

and CDs, two cases can arise. First, some CDs are wholly contained within a single county; in

this case, we directly assign county level variables to CDs, assuming that the effect of Black

in-migration is uniform within the county. Second, some CDs straddle county boundaries. In

such cases, we assign county level values to the CD, weighting them by a county’s area share

of the CD.51 Figure B.1 displays the county (gray lines) to CD (Black lines) mapping just

described for the 78th Congress, restricting attention to non-southern states.

Figure B.1. CD-county Map

Notes: The figure presents a map of counties (gray lines) and Congressional Districts

(Black lines) for the non-South US during the 78th Congress.

50The only difference with their procedure is that we use counties rather than CZs.51Following Feigenbaum and Hall (2015), we test the robustness of our results using other weights, such as

maximum area.

83

B.2 Time Invariant CD Crosswalk

Until the early 1960s, there was no pre-determined rule mandating states to redraw CD bound-

aries after each decennial Census. Moreover, especially in the North, gerrymandering was

substantially less common than it is today (Snyder and Ansolabehere, 2008). Between 1900

and 1964, despite major demographic shifts induced by international and internal migration

(Boustan et al., 2014), redistricting across non-southern districts was typically non-strategic

(Engstrom, 2013). If anything, the lack of systematic redistricting rules likely introduced a pro-

rural bias: more densely populated areas (i.e. urban areas) grew gradually under-represented

at the CD level, likely diluting the effects of Black inflows, which were concentrated in urban

centers (see Figure 1 in the main text).52 However, even during the 1940-1965 period, the

boundaries of many CDs were changed, often multiple times. To overcome this empirical chal-

lenge, we develop a procedure that allows us to match all CDs between 1930 and 1970 to a

baseline Congress.53

We define the 78th Congress (January 6, 1943 to December 19, 1944) as our baseline

Congress year for two main reasons. First, although the 76th Congress might have been a

more natural choice (as it corresponds to the 1940 Census year), several CDs underwent re-

districting between this Congress year and the 78th Congress. In contrast, very few states

redistricted between the 78th and the 82nd Congress. Second, Congress 78th is the earliest

Congress for which CD-level population estimates are available from Adler (2003), thus allow-

ing us to benchmark the population figures estimated in our procedure with other measures.

We thus rely on Congress 78 as our baseline year, and consider the following two Congress

periods: 78 to 82, which we match to the 1940 to 1950 Census decade; and, 83 to 88, which

we match to the 1950 to 1960 year.54 We perform a number of robustness checks to show

that our results do not depend on the choice of the baseline Congress year, and that they are

qualitatively similar when restricting the sample to CDs that did not undergo redistricting over

the 78 to 82 Congress period.

Using this timing convention, for every Congress between 71 and 91, we perform a spatial

merge between CD maps and the map corresponding to the 78th Congress. Then, political

outcomes (e.g. ideology scores, number of discharge petitions signed by legislators, etc.) are

collapsed to the 78th Congress using a weighting procedure similar to that adopted when

matching counties to CDs. The logic of our strategy is simple: we fix the 1944 (i.e. the 78th

Congress) geography of CDs, and we link them to CDs that represented the same geographic

area in subsequent (or previous) Congress years.55 Then, we calculate a weighted average of

political outcomes that correspond to the area originally represented by CDs according to the

1944 map.

To illustrate this procedure, we ask how the 78th Congress would have looked like, had

52This observation suggests that our analysis should identify a lower bound for the effects of Black inflows onlegislators’ (pro-civil rights) behavior.

53While our analysis focuses on years after 1940, we also construct the cross-walk for the pre-1940 decade inorder to perform several robustness and falsification checks.

54The reason to consider the 88th Congress in the second decade is that this was the Congress that approvedthe CRA.

55When states have more than one district, we drop at-large Congressional seats from the spatial merge (e.g.at-large seats for the state of New York are dropped between 1933 and 1945).

84

its geography persisted until Congress 86. We now explain how we proceed to collapse the

political outcomes corresponding to the geography of Congress 86 “back” to that of Congress

78. Suppose that the area represented by a single CD in Congress 78 gets split in two separate

CDs by Congress 86. To assign political variables of new CDs back to the level of the original

CD, we adopt a weighting procedure, based on weights constructed in four steps. First, we

overlay the map of the initial CD to that of the two CDs in Congress 86, and divide the area

in cells derived by this spatial merge. Second, we assign the 1940 county population to each

cell in proportion to the area share of the cell that is included in the county. Third, we sum

over all cells that compose the CD to obtain an estimate of CD population as of Congress 78.

Finally, we divide the area of each cell by such estimated CD population.

Political variables corresponding to the geography of the 78th Congress for subsequent

Congress years are computed by taking the weighted average of the outcomes of the newly

formed CDs, using the weights constructed as explained above. In Appendix C, we validate

the accuracy of this approach by replicating our (baseline) county-level results for the Demo-

cratic vote share using CD level data from Swift et al. (2000). Reassuringly, when conducting

the analysis at the CD level, results remain qualitatively and quantitatively similar to those

reported in the main text (see Table 2).

85

C Appendix – Robustness Checks

In this section we present a variety of robustness checks. First, we show that the instrument

is uncorrelated with county-specific “pull factors” that might have influenced pre-1940 Black

settlements, and verify that results are unchanged when interacting year dummies with a

variety of 1940 county characteristics. Second, we document that our findings are not driven

either by pre-existing trends or by the simultaneous inflow of southern born white migrants.

Third, we show that results are robust to omitting potential outliers, considering alternative

proxies for support for the Democratic Party, and estimating different specifications. Fourth,

we construct an alternative version of the instrument that predicts Black out-migration from

each southern state exploiting only variation across local push factors. Finally, we document

that CD-level results: i) are robust to using different timing conventions to map Black inflows

to Congress periods; ii) are not influenced by pre-existing trends; and, iii) are unlikely to be

driven by strategic gerrymandering.

C.1 Initial Shares, County Characteristics, and Local Shocks

In Table C.1, we start by investigating if the instrument constructed in equation (2) in the

main text is correlated with county-specific pull factors, such as WWII contracts, New Deal

spending, and the vote share of Franklin Delano Roosevelt (FDR) in 1932 Presidential elections.

As discussed in Boustan (2016), the surge in demand across northern and western factories

triggered by WWII was one of the pull factors of the Great Migration. Similarly, the generosity

of New Deal spending and local support for FDR might have influenced the location decision of

African Americans prior to 1940, while at the same time having long-lasting effects on political

conditions across northern counties.

The dependent variable in Table C.1 is the change in predicted Black in-migration, scaled

by 1940 county population. The main regressor of interest is one of the three variables described

above – WWII spending per capita (Panel A), generosity of New Deal (Panel B), and 1932

FDR vote share (Panel C). Columns 1 to 3 consider each decade separately, whereas column 4

focuses on the long difference (1940-1970) change in predicted Black in-migration. We always

include the set of controls used in our most preferred specification – i.e., state dummies, the

1940 Black share, and a dummy equal to 1 if in 1940 the Democratic vote share was higher than

the Republican vote share in Congressional elections – and weigh regressions by 1940 county

population. Reassuringly, in all cases the coefficient is imprecisely estimated and quantitatively

small.

86

Table C.1. Placebo Checks

Dep. Variable Predicted Change in Black Share (Mean: 0.916)

(1) (2) (3) (4)

Panel A. WWII (Mean: 0.648)

Spending per capita 0.041 0.051 0.043 0.092

(0.035) (0.043) (0.037) (0.107)

Observations 1,139 1,139 1,140 1,139

Panel B. New Deal (Mean: 0.143)

Spending per capita -0.284 -0.268 -0.148 -0.514

(0.183) (0.225) (0.192) (0.525)

Observations 1,139 1,139 1,140 1,139

Panel C. Vote Share FDR (Mean: 53.93)

1932 Vote Share FDR -0.005 -0.004 -0.003 -0.009

(0.004) (0.004) (0.003) (0.010)

Observations 1,117 1,115 1,116 1,117

Decade 1940-1950 1950-1960 1960-1970 1940-1970

Notes: The dependent variable is the change in the predicted number of Black migrants over 1940 county

population. Each column considers the period specific to the decade reported at the bottom of the table. All

regressions are weighed by 1940 county population, and control for state dummies, for the 1940 Black share, and

for a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share. In

Panels A, B, and C the main regressor of interest is WWII spending per capita, New Deal spending per capita,

and the vote share for FDR in the 1932 Presidential elections. Robust standard errors, clustered at the county

level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

Next, to address concerns that 1940 Black settlements (from each southern state) might

be correlated with county-specific characteristics that may have had a time varying effect on

changes in political conditions, we interact period dummies with several 1940 county charac-

teristics. Column 1 of Table C.2 and Table C.3 replicates the baseline specification estimated

in Tables 2 and 3 (column 6) in the main text. For completeness, we also report first stage

estimates at the bottom of each table. Columns 2 to 6 augment the baseline specification by

including interactions between period dummies and, respectively, the 1940: i) urban share; ii)

share of employment in manufacturing; iii) male employment to population ratio; iv) fraction

of immigrants; and, v) county population. Reassuringly, the coefficient remains stable and, for

both the Democratic vote share and CORE demonstrations, highly statistically significant.56

In column 7, we augment the baseline specification by separately controlling for a predicted

measure of labor demand growth constructed using a Bartik-type approach (similar to e.g.

Sequeira et al., 2020, and Tabellini, 2020). Restricting attention to non-southern counties, we

56In columns 5 and 6, the KP F-stat falls below conventional levels, due to the stringent nature of the exerciseperformed.

87

first compute the 1940 share of employment in each 1-digit industry in each county; then, we

interact these initial shares with the national growth rate of employment in that industry.57

Once again, results are quantitatively similar to, in fact slightly larger than, those reported in

column 1.

Finally, we deal with the possibility that the 1940 share of Blacks from each southern

state were not independent of cross-county pull factors systematically related to settlers’ state

of origin (Goldsmith-Pinkham et al., 2020). To do so, we replicate our county-level results

by interacting year dummies with the share of Blacks from each southern state, i.e. shsc in

equation (2) in the main text. In Figures C.1, C.2, and C.3, we plot 2SLS coefficients for the

effects of changes in the Black share onthe change in the Democratic vote share, in turnout, and

in CORE demonstrations respectively. The very first dot on the left of the graphs represents the

coefficient from our baseline specification (see also column 6 in Tables 2 and 3). Reassuringly,

both the precision and the magnitude of our estimates are stable across specifications.

57To more precisely proxy for labor demand shocks in non-southern industries, we compute the nationalgrowth rate for the non-South only. Results are unchanged when including the US South to compute nationaldemand growth.

88

Table C.2. Congressional Elections: Controlling for 1940 County Characteristics

(1) (2) (3) (4) (5) (6) (7)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Panel A: Democratic Vote Share

Change Black Share 1.650*** 2.337*** 1.673*** 1.636*** 1.700*** 2.304*** 1.920***

(0.286) (0.555) (0.363) (0.279) (0.456) (0.718) (0.417)

Panel B. Turnout

Change Black Share 0.390* 0.795* 0.381 0.394* 0.746 0.874 0.286

(0.235) (0.438) (0.268) (0.235) (0.466) (0.653) (0.280)

Panel C. First Stage

Change Predicted 1.148*** 0.741*** 0.994*** 1.153*** 0.806*** 0.710** 0.999***

Black share (0.311) (0.240) (0.300) (0.311) (0.308) (0.322) (0.312)

Control Baseline Urban Manufacturing Employment Immigrant County Predicted

Share Share to population share population demand growth

F-stat 13.65 9.523 10.96 13.76 6.825 4.857 10.26

Observations 3,418 3,418 3,418 3,418 3,418 3,418 3,391

Notes: The table replicates the main specification (which is also reported in column 1) for results reported in Table 2 (column 6)by including the interaction between period dummies and, respectively, the 1940: i) urban share (column 2); ii) employment share inmanufacturing (column 3); iii) male employment to population ratio (column 4); iv) immigrant share (column 5); v) county population.In column 7, the baseline specification is augmented by separately controlling for a measure of predicted industrial growth constructedwith a Bartik-style strategy described in the text of the Appendix. Panel C reports the first stage for the 2SLS results presented in PanelsA and B. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parenthesis. Significancelevels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

89

Table C.3. CORE Demonstrations: Controlling for 1940 County Characteristics

Dep. variable Change in Pr.(Pro-civil rights demonstration)

(1) (2) (3) (4) (5) (6) (7)

Panel A. Democratic Vote Share

Change Black Share 0.047*** 0.046*** 0.054*** 0.047*** 0.053*** 0.037** 0.032**

(0.011) (0.017) (0.013) (0.011) (0.018) (0.017) (0.014)

Panel B. First Stage

Predicted Change 1.148*** 0.741*** 0.994*** 1.153*** 0.806*** 0.710** 0.999***

Black share (0.311) (0.240) (0.300) (0.311) (0.308) (0.322) (0.312)

Control Baseline Urban Manufacturing Employment Immigrant County Predicted

Share Share to population share population demand growth

F-stat 13.65 9.523 10.96 13.76 6.825 4.857 10.26

Observations 3,418 3,418 3,418 3,418 3,418 3,418 3,391

Notes: The table replicates the main specification (which is also reported in column 1) for results reported in Table 3 (column 6)by including the interaction between period dummies and, respectively, the 1940: i) urban share (column 2); ii) employment share inmanufacturing (column 3); iii) male employment to population ratio (column 4); iv) immigrant share (column 5); v) county population.In column 7, the baseline specification is augmented by separately controlling for a measure of predicted industrial growth constructedwith a Bartik-style strategy described in the text of the Appendix. Panel B reports the first stage for the 2SLS results presented in PanelA. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parenthesis. Significance levels:∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

90

Figure C.1. Interacting Year Dummies with Initial Shares: Democratic Vote Share

Notes: The figure plots the 2SLS point estimate (with corresponding 95% confidence

intervals) for the effects of a change in the Black share on the Democratic vote share,

augmenting the baseline specification reported in Table 2 with interactions between

period dummies and the 1940 share of Blacks born in each southern state. The very

first dot on the left reports the coefficient for the baseline specification.

91

Figure C.2. Interacting Year Dummies with Initial Shares: Turnout

Notes: The figure plots the 2SLS point estimate (with corresponding 95% confidence

intervals) for the effects of a change in the Black share on turnout, augmenting the baseline

specification reported in Table 2 with interactions between period dummies and the 1940

share of Blacks born in each southern state. The very first dot on the left reports the

coefficient for the baseline specification.

92

Figure C.3. Interacting Year Dummies with Initial Shares: CORE Demonstrations

Notes: The figure plots the 2SLS point estimate (with corresponding 95% confidence

intervals) for the effects of a change in the Black share on turnout, augmenting the baseline

specification reported in Table 3 with interactions between period dummies and the 1940

share of Blacks born in each southern state. The very first dot on the left reports the

coefficient for the baseline specification.

C.2 Testing for Pre-Trends

In Table C.4, we perform a key placebo check, and show that there is no correlation between pre-

period changes in the outcomes of interest and the (instrumented) change in the Black share.

Since 1942 is the first year in which CORE demonstrations occurred, we conduct this exercise

only for the Democratic vote share and for turnout in Congressional elections.58 Table C.4

reports results for the Democratic vote share (resp. turnout) in columns 1 to 3 (resp. 4 to 6).

To ease comparisons, columns 1 and 4 present the baseline specification (Table 2, column 6);

next, in columns 2 and 5, we replicate our analysis restricting attention to counties for which

“pre-trends” regressions can be estimated. As it appears, while results for the Democratic

vote share become slightly larger, those for turnout double in size and become more precisely

estimated.

Finally, in columns 3 and 6, we turn to the formal test for pre-trends, regressing the 1934 to

1940 change in the Democratic vote share and in turnout against the 1940 to 1970 instrumented

change in the Black share.59 When constructing the “pre-1940” change in political outcomes, we

consider the first election year after 1932 in order to make sure that results are not confounded

by post-New Deal realignment (Caughey et al., 2020; Schickler, 2016). However, our findings

are unchanged when using other election years, such as 1930 or 1932. Focusing on “pre-

58Appendix C.5 below conducts a similar test (at the CD level) for legislators’ ideology.59As noted in the main text, the instrument is scaled by 1940 population so as not to contaminate it with the

potentially endogenous contemporaneous population (Card and Peri, 2016).

93

trends” regressions, reassuringly, the coefficient is not statistically significant and, especially

for the Democratic vote share (column 3), very different from that estimated in the baseline

specification.60

Table C.4. Testing for Pre-Trends: Congressional Elections

Dep. Variable Change Democratic vote share Change Turnout

(1) (2) (3) (4) (5) (6)

Panel A. 2SLS

Change Black 1.650*** 1.954*** -0.400 0.390* 0.809** 0.312

Share (0.286) (0.453) (0.370) (0.235) (0.357) (0.212)

Panel B. First Stage

Change predicted 1.148*** 0.738*** 0.778*** 1.148*** 0.738*** 0.778***

Black share (0.311) (0.229) (0.245) (0.311) (0.229) (0.245)

F-stat 13.65 10.39 10.13 13.65 10.39 10.13

Observations 3,418 3,401 1,138 3,418 3,401 1,138

Specification Baseline Restricted Pre-Trends Baseline Restricted Pre-Trends

Notes: Panel A reports 2SLS estimates for the change in the Democratic vote share (resp. turnout) in Con-

gressional elections in columns 1-3 (resp. 4-6). Columns 1 and 4 report the baseline specification (see Table 2,

column 6), and columns 2 and 5 replicate the baseline specification restricting attention to counties for which

the change in the Democratic vote share and turnout between 1934 and 1940 can be computed. Columns 3

and 6 estimate first difference regressions for the 1934-1940 change in the Democratic vote share and in turnout

against the 1940 to 1970 instrumented change in the Black share. The pre-period is defined using the first

election year after the New Deal election of 1932, i.e. 1934. However, results are unchanged when using other

timing conventions. All regressions are weighed by 1940 county population and include state dummies, the 1940

Black share, and a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote

share. These variables are interacted with year dummies in columns 1-2 and 4-5 (as in the main text). F-stat

is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parenthesis.

Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

C.3 Additional Robustness Checks

C.3.1 Alternative Specifications

In our baseline analysis, we interact year dummies with a dummy equal to 1 if the 1940

Democratic vote share in Congressional elections were greater than the Republican one to allow

counties to be on different trends depending on Democratic incumbency (and potentially deal

with mean reversion). To more flexibly account for initial support for the Democratic Party, in

column 2 of Tables C.5 and C.6, we replicate the baseline analysis (reported in column 1 to ease

comparisons) by interacting the 1940 Democratic vote share with year dummies. Reassuringly,

60For turnout, the point estimate is close to that of the baseline specification for the full sample. Yet, oncecompared to the baseline estimates obtained for the “restricted” sample (column 5), the coefficient is almostone third smaller.

94

all results remain in positive and statistically significant. If anything, they become larger, and

more precisely estimated, especially for turnout (Panel B of Table C.5).61

Next, in column 3 of Tables C.5 and C.6, we replicate the baseline specification by estimating

unweighted regressions. Reassuringly, even though results for turnout are no longer statistically

significant, they remain strong and precisely estimated for both the Democratic vote share and

CORE demonstrations. In this case, the KP F-stat for weak instruments falls slightly below

conventional levels. But, the main message remains unchanged.

C.3.2 Dropping Potential Outliers

As discussed in the main text, some areas of the US North and West, such as Chicago, Detroit,

and Los Angeles, received a disproportionately large inflow of Black migrants between 1940

and 1970. Others, instead, received very few African Americans. In our main analysis we omit

counties with zero Black individuals in 1940, so as to compare counties that received different

numbers of migrants with each other (and exclude from this comparison counties that did not

have any Black resident in 1940). We now show that all results are robust to restricting the

sample in different ways.

First, in columns 4 and 5 of Tables C.5 and C.6, we restrict attention to counties for which

the predicted and the actual Black share was strictly positive in all decades between 1940 and

1970. Not surprisingly, results are unchanged. Next, in column 6 (resp. 7), we exclude counties

at the top 1st (resp. 5th) and at the bottom 99th (resp. 95th) percentiles of the distribution

of changes in Black migration. Also in this case, results remain in line with – in fact stronger

than – those of our baseline specification.

C.3.3 Controlling for Southern White In-Migration

Yet another potential concern is that Black in-migration might be correlated with simultaneous

white inflows from the South. As documented in Gregory (2006) among others, between 1940

and 1970 even more whites than Blacks left the US South. The historical evidence suggests that

African Americans were significantly more likely than whites to settle in metropolitan areas

either in the Northeast or in the West, while white migration was more evenly distributed

across the non-South (Gregory, 1995). However, it is still possible that the patterns of white

and Black migration from the South were correlated with each other. If this were to be the

case, at least part of our findings might be due to the arrival of white – rather than Black –

migrants. Due to data limitations, we cannot measure the actual change in southern born white

migrants after 1940 at the county level. However, to at least partly overcome this problem,

we construct a predicted measure of white in-migration from the US South implementing the

same procedure used to construct the instrument for Black in-migration (see equation (2) in

the main text).

Specifically, we first compute the share of whites born in each southern state who were

living in a non-southern county as of 1940. Next, we interact these shares with the number

61Reassuringly, the first stage, reported in the bottom panel of both tables, remains strong and the KP F-statfor weak instruments above conventional levels.

95

Table C.5. Additional Robustness Checks: Congressional Elections

(1) (2) (3) (4) (5) (6) (7) (8)

Panel A. Democrat Vote Share

Change Black Share 1.650*** 2.095*** 1.936*** 1.649*** 1.650*** 1.757*** 2.662*** 1.534***

(0.286) (0.424) (0.555) (0.286) (0.288) (0.377) (0.651) (0.243)

Panel B. Turnout

Change Black Share 0.390* 0.690** 0.198 0.392* 0.394* 0.515* 0.700* 0.356*

(0.235) (0.301) (0.328) (0.236) (0.234) (0.292) (0.383) (0.215)

Panel C. First Stage

Predicted Change 1.148*** 0.808*** 0.392*** 1.147*** 1.146*** 0.981*** 0.762*** 1.250***

Black Share (0.311) (0.232) (0.137) (0.311) (0.311) (0.286) (0.246) (0.290)

F-stat 13.65 12.11 8.199 13.61 13.58 11.80 9.597 18.62

Specification Baseline 1940 Dem vote Unweighted Drop IV Drop Black Trim top 99 and Trim top 95 and Southern White

share equal to 0 share equal to 0 bottom 1 pctile bottom 5 pctile in-Migration

Observations 3,418 3,414 3,418 3,295 3,251 3,342 3,081 3,418

Notes: The table replicates the main specification (which is also reported in column 1) for results reported in Table 2 (column 6) by: i) replacing the interaction between perioddummies and the 1940 Democratic incumbency dummy with that with the 1940 Democratic vote share in Congressional elections (column 2); ii) estimating unweighted regressions(column 3); iii) considering only counties with predicted (resp. actual) Black share strictly positive in all decades in column 4 (resp. column 5); iv) trimming counties at the top 1st

(resp. 5th) and at the bottom 99th (resp. 95th) percentiles of the distribution of changes in Black migration in column 6 (resp. column 7); and v) controlling for predicted southernwhite in-migration (column 8). Panel C reports the first stage for the 2SLS results presented in Panels A and B. F-stat is the K-P F-stat for weak instruments. Robust standarderrors, clustered at the county level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ** p< 0.05,* p< 0.1.

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Table C.6. Additional Robustness Checks: CORE Demonstrations

(1) (2) (3) (4) (5) (6) (7) (8)

Panel A.2SLS

Change Black Share 0.047*** 0.056*** 0.032** 0.047*** 0.047*** 0.068*** 0.051** 0.046***

(0.011) (0.016) (0.013) (0.011) (0.011) (0.018) (0.021) (0.010)

Panel B. First Stage

Predicted Change 1.148*** 0.808*** 0.392*** 1.147*** 1.146*** 0.981*** 0.762*** 1.250***

Black Share (0.311) (0.232) (0.137) (0.311) (0.311) (0.286) (0.246) (0.290)

F-stat 13.65 12.11 8.199 13.61 13.58 11.80 9.597 18.62

Specification Baseline 1940 Dem vote Unweighted Drop IV Drop Black Trim top 99 and Trim top 95 and Southern White

share equal to 0 share equal to 0 bottom 1 pctile bottom 5 pctile in-Migration

Observations 3,418 3,414 3,418 3,295 3,251 3,342 3,081 3,418

Notes: The table replicates the main specification (which is also reported in column 1) for results reported in Table 3 (column 6) by: i) replacing the interaction betweenperiod dummies and the 1940 Democratic incumbency dummy with that with the 1940 Democratic vote share in Congressional elections (column 2); ii) estimating unweightedregressions (column 3); iii) considering only counties with predicted (resp. actual) Black share strictly positive in all decades in column 4 (resp. column 5); iv) trimmingcounties at the top 1st (resp. 5th) and at the bottom 99th (resp. 95th) percentiles of the distribution of changes in Black migration in column 6 (resp. column 7); and v)controlling for predicted southern white in-migration (column 8). Panel B reports the first stage for the 2SLS results presented in Panel A. F-stat is the K-P F-stat for weakinstruments. Robust standard errors, clustered at the county level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ** p< 0.05,* p< 0.1.

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of white migrants from each southern state in each decade between 1940 and 1970. Finally,

for each non-southern county and for each decade, we sum the predicted number of whites

moving from each origin over all southern states to obtain the total number of (predicted)

white migrants moving to county c during decade τ . In formulas, this measure is given by:

ZWcτ =∑

j∈SouthshwjcWhjτ (3)

where shwjc is the share of whites born in southern state j and living in non-southern county c

in 1940, relative to all whites born in j living outside this state; and Whjτ is the number of

whites who left southern state j during decade τ .

Then, in column 8 of Tables C.5 and C.6, we augment our baseline specification by sep-

arately controlling for the predicted southern white in-migration. Reassuringly, in all cases,

results are barely affected.

C.3.4 Additional Outcomes

In the paper, we focus on the Democratic vote share as the main electoral outcome of interest.

In Table C.7, we verify that results are unchanged when considering different proxies for support

for the Democratic Party in Congressional elections. Column 1 presents our main 2SLS results

for the Democratic vote share (Table 2, column 6). Next in columns 2 and 3, the dependent

variable is defined respectively as the Democratic vote margin and as a dummy equal to 1 if

the Democratic vote share was larger than the Republicans vote share. In both cases, Black

in-migration is associated with an increase in support for the Democratic Party.

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Table C.7. Additional Outcomes: Congressional Elections

Dep. variable Democratic Democrats-Republicans 1[Higher Democratic

Vote Share Vote Margin Vote Share]

(1) (2) (3)

Panel A.2SLS

Change Black Share 1.650*** 3.184*** 0.031***

(0.286) (0.555) (0.010)

Panel B. First Stage

Predicted Change 1.148*** 1.147*** 1.152***

Black Share (0.311) (0.309) (0.312)

F-stat 13.65 13.79 13.62

Observations 3,418 3,401 3,325

Notes: Panel A presents 2SLS results. Column 1 replicates the baseline specification for the effects of changes

in the Black share on the Democratic vote share (Table 2, column 6). In columns 2 and 3, the dependent variable

is, respectively, the Democrats-Republicans vote margin in Congressional elections, and a dummy equal to 1

if the Democratic vote share was higher than the Republicans vote share in Congressional elections. Panel B

reports the first stage. All regressions are weighed by 1940 county population and include interactions between

year dummies and: i) state dummies; ii) the 1940 Black share; and iii) a dummy equal to 1 if the Democratic

vote share in 1940 was higher than the Republicans vote share.F-stat is the K-P F-stat for weak instruments.

Robust standard errors, clustered at the county level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ** p< 0.05,

* p< 0.1.

C.3.5 Stacked Panel Specification

Finally, we verify that our results are robust to estimating stacked panel regressions separately

controlling for county fixed effects, rather than taking the model in (stacked) first differences.

Specifically, we stack the data for the four decades between 1940 to 1970 (included), and run

a regression of the form:

yct = ξc + δst + βBlct + γXct + uct (4)

where yct refers to the Democratic vote share and turnout in Congressional elections or to the

probability of CORE demonstrations in county c in year t, ξc and δst are county and state by

year fixed effects, and Blct is the Black share in county c in year t. As for the stacked first

difference specification, Xct includes interactions between year dummies and baseline: i) Black

share; and ii) Democratic incumbency in Congressional elections.62

In our baseline specification, we used predicted Black inflows in each decade to instrument

for the change in Black population. However, when estimating equation (4), an instrument is

needed for Black population in each year from 1940 to 1970. That is, 1940 can no longer be

used as “baseline” year to predict Black inflows. Also, since we are now interested in Black

population (relative county population) rather than in its change, we need an instrument for the

62Since in a stacked panel setting 1940 is our first estimation year, we measure the baseline Black share in1930 and the Democratic incumbency in 1934. Results are unchanged if we measure both variables in 1940.

99

stock – and not the change – of Blacks in the county. We thus modify the baseline instrument

constructed in the main text in two ways. First, we use 1930 settlements of African Americans

across northern counties to apportion post-1930 outmigration from the South. Second, after

predicting the inflow of Blacks to county c in the ten years prior to year t, we recursively add

previous predicted inflows to generate a measure of predicted stock.63

With this instrument at hand, we proceed to estimate equation (4) with 2SLS. We report

results in Panel A of Table C.8, presenting first stage in Panel B. Focusing on Democratic vote

share, turnout, and CORE demonstrations respectively, we report the baseline (stacked first

difference) specification in columns 1-3-5 to ease comparisons, and the stacked panel regressions

in columns 2-4-6. The KP F-stat for weak instrument falls below conventional levels when

estimating the stacked panel specifications. However, the main take-away is unchanged. Black

in-migration has a positive and statistically significant effect on the Democratic vote share and

on the frequency of CORE demonstrations, and a small and imprecisely estimated effect on

turnout.

63As before, we scale the predicted number of Blacks by 1940 county population. Results are unchanged whendividing it by 1930 population.

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Table C.8. Stacked Panel Specification

Dep. variable Democratic Vote Share Turnout 1[CORE Demonstrations]

(1) (2) (3) (4) (5) (6)

Panel A. 2SLS

Change Black Share 1.650*** 1.394*** 0.390* 0.128 0.047*** 0.045***

(0.286) (0.413) (0.235) (0.172) (0.011) (0.015)

Panel B. First stage

Predicted Change 1.148*** 1.118*** 1.148*** 1.118*** 1.148*** 1.118***

Black Share (0.311) (0.405) (0.311) (0.405) (0.311) (0.405)

F-stat 13.65 7.438 13.65 7.438 13.65 7.438

Observations 3,418 4,328 3,418 4,328 3,418 4,328

Specification Stacked first Stacked panel Stacked first Stacked panel Stacked first Stacked panel

differences diferences differences

Notes: : The table replicates the baseline stacked first difference specification using a stacked panel specification. The dependent

variable is the (resp. the change in) Democratic vote share, turnout, and probability of CORE demonstrations in columns 2-4-6

(resp. in columns 1-3-5). Panel A reports 2SLS estimates, and Panel B presents the first stage. All regressions are weighed by 1940

county population. Columns 1-3-6 include interactions between year dummies and: i) state dummies; ii) the 1940 Black share; and,

iii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share. Columns 2-4-6 include

county and state by year fixed effects, and control for interactions between year dummies and: i) the 1930 Black share; and, ii) a

dummy equal to 1 if the Democratic vote share in 1934 was higher than the Republicans vote share. Results in columns 2-4-6 are

unchanged when including interactions using 1940 (rather than pre-1940 values). F-stat is the K-P F-stat for weak instruments.

Robust standard errors, clustered at the county level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

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C.4 Push Factors Instrument

C.4.1 Instrument Construction and Zeroth Stage

Borusyak et al. (2018) note that the validity of shift-share designs can be guaranteed if the

“shifts” – in our case, decadal Black migration from each southern state – are exogenous to

local conditions. They propose a correction method, where the “shift-share” instrument is

expressed in terms of the “shift” components. This method, however, can be implemented

only when the number of “shifts” is large. Unfortunately, in our setting, we can only rely on 15

southern states, and so we cannot directly implement the transformation proposed in Borusyak

et al. (2018).

Nevertheless, we provide evidence that southern (state) migration flows are orthogonal to

northern (county) conditions. We construct a modified version of the instrument that, rather

than using actual Black out-migration, estimates it exploiting variation solely induced by local

push factors. Following Boustan (2010, 2016) and Derenoncourt (2018), we model emigration

from each southern county for each decade between 1940 and 1970 as a function of local push

factors. In particular, we estimate an equation of the form

migkjτ = αj + βτPushkjt0 + ukjτ (5)

where migkjτ is the Black net migration rate in county k of southern state j during decade τ ,

and Pushkjt0 is a vector of economic and political conditions at baseline, which we allow to have

a time-varying effect across decades. These include the 1940: share of land cultivated in cotton;

share of farms operated by tenants; share of the labor force in, respectively, manufacturing,

mining, and agriculture. As in Boustan (2016), we also include WWII spending per capita and

the 1948 vote share of Strom Thurmond in Presidential elections.64

Our most preferred specification includes state fixed effects, αj , but results are unchanged

when omitting them (see also Boustan, 2016). Finally, in contrast with Boustan (2010, 2016),

we fix the characteristics of southern counties to 1940 (or, for Thurmond vote share, 1948)

rather than using the beginning of each decade to reduce concerns of correlated shocks be-

tween northern and southern counties.65 As an additional robustness check, we also selected

the southern county characteristics to predict Black out-migration using the Least Absolute

Shrinkage and Selection Operator (“LASSO”), as done in Derenoncourt (2018). Below, we

report results obtained using this alternative procedure to construct the “push” version of the

instrument.

Results from equation (5) are reported in Table C.9. Columns 1 to 3 refer to, respectively,

the 1940-1950, the 1950-1960, and the 1960-1970 decade. All coefficients have the expected

sign. A higher share of land in cotton and of farms operated by tenants in 1940 are associated

with subsequent emigration. Somewhat surprisingly, however, the coefficient is not statistically

significant for the 1940-1950 decade, possibly because cotton mechanization was more prevalent

64Data on the cotton share comes from the Census of Agriculture, the vote share of Thurmond was takenfrom David Leip’s Atlas, while all remaining variables were collected from the County Databooks.

65Following Boustan (2016), in counties where the Black migration rate was above 100, we replace it withthe latter value. We also exclude counties with less than 30 Black residents in 1940. All results are robust toomitting these restrictions.

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Table C.9. Zeroth Stage

Dep. Variable Net Black Migration Rate

(1) (2) (3)

Share Land in Cotton -0.012 -0.302∗∗ -0.163∗∗

(0.088) (0.123) (0.077)

Share Farms with Tenants 0.042 0.045 -0.173∗∗∗

(0.056) (0.064) (0.047)

WWII Spending per Capita 2.228∗∗∗ 0.393 0.046

(0.352) (0.359) (0.313)

Thurmond Vote Share -0.085∗∗ -0.083∗∗ -0.158∗∗∗

(0.037) (0.037) (0.042)

Share LF in Manufacturing -0.348∗∗∗ -0.248∗∗∗ -0.080

(0.090) (0.074) (0.070)

Share LF in Mining -0.440∗∗ -0.697∗∗∗ -0.522∗∗∗

(0.197) (0.179) (0.152)

Share LF in Agriculture -0.504∗∗∗ -0.486∗∗∗ -0.209∗∗∗

(0.050) (0.047) (0.045)

State Fixed Effects X X X

R-squared 0.256 0.283 0.163

Observations 1,163 1,163 1,163

Decade 1940-1950 1950-1960 1960-1970

Notes: The dependent variable is the net Black migration rate for southern counties for each decade indicatedat the bottom of the table. All regressors refer to 1940, except for Thurmond vote share, which is the vote shareof Thurmond in 1948 Presidential elections. All regressions include state fixed effects. See the appendix for thedefinition and source of variables included in the table. Robust standard errors, clustered at the county level,in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

in the 1950s (Grove and Heinicke, 2003). As in Boustan (2016), a higher share of the labor

force in mining and agriculture is associated with a larger emigration rate. Similarly, reflecting

a more hostile political environment, counties with a higher vote share for Thurmond in 1948

are predicted to have a higher emigration rate throughout the period. Finally, consistent with

WWII spending increasing labor demand, the Black in-migration rate is higher in counties with

more WWII contracts.

After estimating equation (5), we construct the predicted number of migrants by multiplying

the fitted values from (5) by the beginning of decade Black population. We then aggregate

these (predicted) flows to obtain the predicted number of Black migrants from each state in

each decade, Blsτ . Finally, we replace the actual number of Black migrants, Blsτ , with this

predicted value to construct a modified version of the shift-share instrument in equation (2) in

the main text.

C.4.2 Results

Table C.10 replicates our preferred specification for the Democratic vote share (columns 1-

2), turnout (columns 3-4), and CORE demonstrations (columns 5-6) using the push-factor

version of the instrument. In Panel A, we present 2SLS estimates, while in panel B we present

103

the associated first stage. Columns 1-3-5 report results obtained using the push instrument

constructed with the southern characteristics described above in the zeroth stage. Columns

2-4-6 turn to the version of the push instrument obtained by selecting predictors of southern

Black out-migration with the LASSO procedure (Derenoncourt, 2018).

Reassuringly, both versions of the instrument are strong, with the KP F-stat above con-

ventional levels. Moreover, the 2SLS estimates are in line with – in fact, for the Democratic

vote share and CORE demonstrations, stronger than – those presented in the main text. Also

in this case, Black in-migration has a positive and statistically significant effect on both the

Democratic vote share in Congressional elections and the probability of CORE demonstrations.

The effect on turnout remains positive, but small and imprecisely estimated. Taken together,

this exercise increases the confidence that our main results are not driven by local pull shocks

simultaneously correlated with the pre-1940 distribution of Black settlements across northern

counties.

Table C.10. Replicating Results with Push Instrument

Dep. variable Democratic Vote Share Turnout 1[CORE Demonstrations]

(1) (2) (3) (4) (5) (6)

Panel A. 2SLS

Change Black 1.856*** 2.298*** 0.420* 0.481* 0.050*** 0.056***

share (0.331) (0.504) (0.233) (0.276) (0.012) (0.013)

Panel B. First stage

Predicted Change 1.289*** 1.025*** 1.289*** 1.025*** 1.289*** 1.025***

Black Share (0.348) (0.303) (0.348) (0.303) (0.348) (0.303)

F-stat 13.68 11.40 13.68 11.40 13.68 11.40

Observations 3,418 3,418 3,418 3,418 3,418 3,418

Push Instrument Baseline LASSO Baseline LASSO Baseline LASSO

Notes: The table replicates the baseline specification using the version of the instrument constructed with

southern specific “push” factors. Columns 1-3-5 (resp. columns 2-4-6) report results for the “push” instrument

constructed using the baseline (resp. LASSO) procedure. The dependent variable is the change in Democratic

vote share, turnout, and probability of CORE demonstrations. Panel A reports 2SLS estimates, and Panel B

presents the first stage. All regressions are weighed by 1940 county population, and include interactions between

year dummies and: i) state dummies; ii) the 1940 Black share; and, iii) a dummy equal to 1 if the Democratic

vote share in 1940 was higher than the Republicans vote share. F-stat is the K-P F-stat for weak instruments.

Robust standard errors, clustered at the county level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05,∗ p< 0.1.

C.5 Robustness Checks on CD Results

C.5.1 Testing for Pre-Trends

Our main results in Table 7 showed that Black in-migration moved legislators’ ideology to

the left between 1940 and 1950. In Table C.11, we check that this pattern does not capture

104

pre-existing trends. Similar to what we did for the Democratic vote share and turnout in

Congressional elections (Section C.2 above), we construct the pre-period change in the ideology

scores, considering the first Congress after the New Deal, i.e. Congress 73. Then, we estimate

2SLS regressions for the pre-period change in the agnostic and the constrained version of the

scores against the instrumented change in the Black share, controlling for the same variables

included in our baseline specification (i.e. state dummies, and baseline: i) Black share; ii)

Democratic incumbency indicator; and, iii) ideology score).66

Table C.11. Testing for pre-trends: ideology scores

Dep. Variable Agnostic Scores Constrained Scores

(1) (2) (3) (4) (5) (6)

Panel A.2SLS

Change Black Share -0.307** -0.231* 0.037 -0.345*** -0.292* 0.035

(0.121) (0.134) (0.027) (0.130) (0.157) (0.033)

Panel B. First Stage

Predicted Change 1.006*** 0.985* 2.403*** 1.002*** 0.973* 2.416***

Black Share (0.379) (0.536) (0.275) (0.378) (0.535) (0.269)

F-stat 7.068 3.380 76.23 7.038 3.312 80.78

Observations 286 202 202 286 202 202

Specification Baseline Restricted Pre-Trends Baseline Restricted Pre-Trends

(1940-1960) (1940-1960)

Notes: Panel A reports 2SLS estimates for the change in agnostic (resp. constrained) ideology scores in columns

1 to 3 (resp. 4 to 6). Panel B reports first stage estimates. Columns 1 and 4 report the baseline specification

for Congress period 78-82 (see Table 7, columns 2 and 5), and columns 2 and 5 replicate this by restricting

attention to counties for which the change in the scores for the pre-period can be constructed. Columns 3 and

6 estimate 2SLS regressions for the change in the ideology scores between Congress 73 and Congress 78 against

the instrumented 1940-1960 change in the Black share. The pre-period is defined using the first Congress year

after the New Deal (i.e. Congress 73). All regressions are weighed by 1940 CD population, and include state

dummies, and the baseline: Black share, Democratic incumbency dummy, and ideology score. F-stat is the K-P

F-stat for weak instruments. Robust standard errors, clustered at the CD level, in parenthesis. Significance

levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05, ∗ p< 0.1.

To ease comparisons, we report the baseline specification for the 78-82 Congress period –

when Black in-migration induced a liberal shift in legislators’ ideology – in columns 1 and 4 for

the agnostic and the constrained version of the scores respectively. Since the pre-period change

in ideology could not be estimated for all CDs, in columns 2 and 5, we replicate columns 1

and 4 restricting attention to CDs for which the pre-trend check can be performed. When

doing so, the F-stat falls, suggesting that results should be interpreted with caution. However,

the point estimate remains negative, quantitatively close to that obtained for the full sample,

and statistically significant (with a p-value of 0.08 and 0.063 for agnostic and constrained

66As usual, regressions are weighed by baseline CD population.

105

scores respectively). Finally, in columns 3 and 6, we turn to the formal test for pre-trends.

Reassuringly, the point estimate is positive, close to zero, and imprecisely estimated. These

patterns indicate that the main results documented above are not influenced by a spurious

correlation between the instrument and potential pre-existing trends in ideology of legislators.

C.5.2 Alternative Timing Conventions for Congress Periods

In our baseline specification for the effects of the Great Migration on legislators’ ideology, we

mapped the 1940-1950 (resp. 1950-1960) change in the Black share to the 78-82 (resp. 82-88)

Congress period. This was done in order to include the longest periods without redistricting

while at the same time ending the analysis with the Congress that passed the CRA. We now

verify that our results above are robust to different timing conventions.

First, in Table C.12, we define the second period as ending with Congress 87 (rather than

Congress 88). The structure of the table mirrors that of Table 7 in the main text: columns 1 to

3 consider the agnostic version of the scores, while columns 4 to 6 focus on the constrained one.

Panel A reports 2SLS estimates, whereas Panel B presents the first stage. Not only results are

in line with those in the main text. But also, the point estimate for the stacked specification

doubles in size and becomes marginally significant. Second, in Table C.13, we define the end

of the first Congress period with Congress 83, in order to have two symmetric periods. While

the coefficient on the Great Migration remains highly negative and precisely estimated in the

first period, it becomes statistically significant (and positive) in the second period (reinforcing

the results on polarization discussed in Section 6.2.2 in the main text).

106

Table C.12. Changes in Legislators’ Ideology: Ending Analysis with Congress 87

Dep. Variable Change in Civil Rights Ideology (Lower values = more liberal ideology)

Agnostic Scores Constrained Scores

(Baseline mean: -0.872) (Baseline mean: -0.853)

(1) (2) (3) (4) (5) (6)

Panel A.2SLS

Change Black Share -0.094* -0.307** -0.007 -0.092* -0.345*** 0.011

(0.054) (0.121) (0.068) (0.054) (0.130) (0.069)

Panel B. First Stage

Predicted Change 1.473*** 1.006*** 1.811*** 1.456*** 1.002*** 1.785***

Black Share (0.441) (0.379) (0.556) (0.445) (0.378) (0.562)

F-stat 11.15 7.068 10.60 10.72 7.038 10.08

Observations 571 286 285 571 286 285

Congress Period 78-82; 82-87 78-82 82-87 78-82; 82-87 78-82 82-87

Notes: The dependent variable is the change in the civil rights ideology scores from Bateman et al. (2017)

– “Agnostic” scores in columns 1 to 3, and “Constrained” scores in columns 4 to 6. Lower (resp. higher)

values of the score refer to more liberal (resp. conservative) ideology (see also Bateman et al., 2017, for more

details). Columns 1 and 4 (resp. 2-3, and 5-6) estimate stacked first difference regressions (resp. first difference

regressions for Congress period 78-82 and 82-87). Panel A reports 2SLS results, while Panel B presents first stage

estimates. All regressions are weighed by 1940 congressional district population and control for state by year

fixed effects and include interactions between year dummies and: i) the 1940 black share in the congressional

district; ii) a dummy for Democratic incumbency in the 78th Congress in the district; and iii) the ideology score

in the district in the 78th Congress. First difference regressions do not include interactions with year dummies

since these are automatically dropped. F-stat refers to the K-P F-stat for weak instrument. Robust standard

errors, clustered at the congressional district level, in parentheses. Significance levels: ∗∗∗ p< 0.01, ** p< 0.05,

* p< 0.1.

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Table C.13. Changes in Legislators’ Ideology: Symmetric Congress Periods

Dep. Variable Change in Civil Rights Ideology (Lower values = more liberal ideology)

Agnostic Scores Constrained Scores

(Baseline mean: -0.872) (Baseline mean: -0.853)

(1) (2) (3) (4) (5) (6)

Panel A.2SLS

Change Black Share -0.041 -0.456*** 0.127** -0.044 -0.503*** 0.143***

(0.038) (0.165) (0.050) (0.040) (0.175) (0.054)

Panel B. First Stage

Predicted Change 1.471*** 1.006*** 1.808*** 1.454*** 1.002*** 1.782***

Black Share (0.440) (0.377) (0.554) (0.443) (0.377) (0.560))

F-stat 11.20 7.099 10.66 10.76 7.069 10.12

Observations 562 281 281 562 281 281

Congress Period 78-83; 83-88 78-83 83-88 78-83; 83-88 78-83 83-88

Notes: The dependent variable is the change in the civil rights ideology scores from Bateman et al. (2017)

– “Agnostic” scores in columns 1 to 3, and “Constrained” scores in columns 4 to 6. Lower (resp. higher)

values of the score refer to more liberal (resp. conservative) ideology (see also Bateman et al., 2017, for more

details). Columns 1 and 4 (resp. 2-3, and 5-6) estimate stacked first difference regressions (resp. first difference

regressions for Congress period 78-83 and 83-88). Panel A reports 2SLS results, while Panel B presents first stage

estimates. All regressions are weighed by 1940 congressional district population and control for state by year

fixed effects and include interactions between year dummies and: i) the 1940 black share in the congressional

district; ii) a dummy for Democratic incumbency in the 78th Congress in the district; and iii) the ideology score

in the district in the 78th Congress. First difference regressions do not include interactions with year dummies

since these are automatically dropped. F-stat refers to the K-P F-stat for weak instrument. Robust standard

errors, clustered at the congressional district level, in parentheses. Significance levels: ∗∗∗ p< 0.01, ** p< 0.05,

* p< 0.1.

C.5.3 Redistricting, Black Inflows, and Political Outcomes

One potential concern with results in Section 6 is that the decision of redistricting a CD may

have been at least partly driven by the arrival of African Americans. If this were to be the

case, and if redistricting had an effect on political outcomes, our results may be biased. As

noted in Appendix B, until 1964 (i.e. the end of our sample period), redistricting was unlikely

to be strategic (Engstrom, 2013), and was typically mandated at the state level. We exploit

the fact that between Congress 78 and Congress 82, five states in our sample (Arizona, Illinois,

New York, Maryland, and Pennsylvania) required their CDs to redistrict, and test whether

redistricting was systematically correlated with either Black inflows or changes in political

conditions (e.g. party switches, changes in legislators’ ideology, etc.).67

In Table C.14, the dependent variable is a dummy equal to 1 if a CD belongs to a state

67This check cannot be performed between Congress 83 and Congress 88 because most CDs were subject toredistricting in this period.

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that did not mandate redistricting, and is regressed against: i) changes in the Black share

(with OLS in column 1 and with 2SLS in column 2); ii) a dummy if the CD underwent a party

switch; iii) the change in the Bateman et al. (2017) ideology score (column 4); and iv) the

number of discharge petitions signed per legislator (column 5). Since the dependent variable

varies at the state level, we cannot control for state fixed effects; yet, we include (as in our

baseline specifications) the 1940 Black share and the 1940 Democratic dummy. Reassuringly,

the coefficient is never statistically significant, does not display any systematic pattern, and

is always quantitatively small. Overall, this exercise thus suggests that neither changes in

the Black share nor changes in political conditions were systematically associated with state-

mandated redistricting.

Next, we inspect more directly the possibility that Black inflows led to strategic gerry-

mandering across CDs. In particular, we rely on the measure of (non-)compactness recently

introduced by Kaufman et al. (2017), which is based on the geographic shape of CDs, and

captures the “compactness evaluations” made by judges and public officials responsible for re-

districting.68 We prefer to use this measure, instead of an alternative proxy based on the vote

distribution, because it provides evidence of (potential) gerrymandering at the CD level. In

turn, this allows us to investigate the relationship between non-compactness and Black inflows.

The measure of compactness can take values between 1 and 100, with higher values indicating

less compact districts, i.e. a higher probability of gerrymandering.

We start by analyzing descriptively the trends of non-compactness between Congress 71 and

Congress 90 in Figure C.4. Consistent with the existing literature discussed in Appendix B,

for the period considered in our analysis – between Congress 78 and Congress 88 – average

compactness changes very little. Reassuringly, other aggregate measures, such as the standard

deviation and the interquantile range, do not show any detectable changes either (not shown).

Interestingly, and again consistent with existing studies, non-compactness starts to increase

precisely after Congress 88, suggesting that after 1964 strategic gerrymandering might have

become gradually more common.

Then, we study the relationship between Black inflows and non-compactness during our

sample period. To do so, we proceed as follows. First, we assign the 1940-1950 (resp. 1950-

1960) change in the Black share to each Congress in the 78-82 (resp. 83-88) Congress period.

Second, we estimate 2SLS regressions where the dependent variable is the measure of non-

compactness specific to each Congress number (for the relevant decade) and the main regressor

of interest is the instrumented change in the Black share. Figure C.5 reports the implied

2SLS coefficients (with corresponding 95% confidence intervals) from previous regressions cor-

responding to a one standard deviation change in the Black share.

If the arrival of African Americans induced northern politicians to strategically change

the boundaries of CDs, we would expect the association between changes in the Black share

and non-compactness to increases over time. Reassuringly, there is no statistically significant

relationship between the change in the Black share and the measure of non-compactness in

any Congress year. Our estimates are also quantitatively small. For instance, one standard

deviation increase in the Black share (around 2.8 percentage points) increases compactness of

68We thank the authors for making their codes available to us.

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Congress 78 by 2 points – a negligible effect when compared to a mean of 45 and to a standard

deviation of 16. Moreover, coefficients do not display any increasing trend over time, suggesting

that strategic gerrymandering in response to Black arrivals was very unlikely to occur during

our sample period.

Table C.14. Redistricting Checks

Dep. Variable 1[Non-Redistricting State]

(1) (2) (3) (4) (5)

Change Black Share 0.014 0.039

(0.013) (0.038)

Party Switch 0.084

(0.061)

Change Ideology Scores -0.007

(0.049)

Signatures on Discharge Petitions -0.035

(0.023)

F-stat 17.31

Observations 286 286 286 286 298

Notes: The dependent variable is a dummy equal to 1 if the CD belongs to a state that did not mandate

redistricting between Congress 78 and Congress 82. In columns 1 and 2, the main regressor of interest is the

change in the Black share during the 1940-1950 decade. Column 1 (resp. column 2) presents OLS (resp. 2SLS)

results. Columns 3, 4, and 5 regress the redistricting state dummy against, respectively, a dummy equal to 1 if

the CD experienced a party transition during the 78-82 Congress period, the change in Bateman et al. (2017)

scores, and the signatures on discharge petitions per legislator. All regressions control for the 1940 Black share,

and for a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share.

Robust standard errors, clustered at the CD level, in parenthesis. Significance levels: ∗∗∗ p< 0.01, ∗∗ p< 0.05,∗ p< 0.1.

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Figure C.4. Average Non-compactness, Congress 71st-90th

Notes: The figure presents the area-weighted average non-compactness for each

Congress between Congress 71 and Congress 90. The red, vertical lines, correspond-

ing to Congresses 78 and 88 isolate the sample period considered in our paper.

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Figure C.5. Black In-Migration and Non-compactness

Notes: The figure presents the 2SLS coefficient with the corresponding 95% confi-

dence interval implied by one standard deviation change in the Black share during

the corresponding decade. The dependent variable is the CD non-compactness score

from Kaufman et al. (2017). The main regressor of interest is the 1940 to 1950 (resp.

1950 to 1960) change in the Black share for Congresses between 78 and 82 (resp.

between 83 and 88), and is instrumented with the shift-share instrument described

in the text. All regressions control for state dummies, for the 1940 Black share,

and for a dummy equal to 1 if the district was represented by a Democrat in each

Congress.

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D Appendix – Survey Data

D.1 The American National Election Studies (ANES)

The American National Election Studies (ANES) is a cross-sectional, nationally representative

survey conducted since 1948 by the University of Michigan every two or four years depending

on the waves. As noted in Gentzkow (2016), the ANES is considered the “gold standard”

when it comes to measure political ideology and cultural or social attitudes of Americans in

the second part of the twentieth century. The ANES asks questions on demographics, party

affiliation, political attitudes, and ideology. Moreover, and crucially for our purposes, since the

mid-late 1950s, respondents are asked about their views on civil rights legislation and racial

equality and, in some instances, about their attitudes towards integration.69

In each wave, between 1,500 and 2,000 respondents were interviewed. Restricting the

sample to whites living in non-southern states leaves us with an average of roughly 1,000

individuals for whom we can consistently include the following controls: marital status, gender,

and fixed effects for education and age.70 In principle, additional characteristics, such as

union status, party affiliation and identification, and state of birth, are available. Since these

may be endogenous to Black migration, however, we do not control for them in our baseline

specification. Most of our analysis uses data from the surveys of 1960 and 1964, but, in a

few cases, we were able to obtain data also from other years. Before 1978, the ANES never

reported the county, but only the state, of residence of respondents. For this reason, our

analysis is conducted at the state level.

Table D.1 presents the questions considered to measure racial attitudes and views towards

civil rights. The first column presents the name of the variable; the second one includes the

exact wording of the question; and the last column lists the years for which the question was

available. The first variable listed, “Most Important Problem” refers to an open-ended ques-

tion in which respondents were asked what they considered (up to) the three most important

problems for the US in the year of the survey. From such open-ended question the ANES

created one specific category that includes racial and public order related issues. For 1960 and

1964, the ANES coded respondents’ answers in categories that reflected their attitudes towards

civil rights and integration.71 We use the ANES pre-classified category “Pro integration - anti

discrimination in schools, employment, etc.” to create a dummy equal to one if the respon-

dent believes that promoting integration in schools, employment, etc. is one of the top three

problems facing the country in that survey year. This is the variable 1[Pro Civil Rights: Most

Important Problem] considered in Table 5 in the main text.

Table 5 verifies that the variable 1[Pro Civil Rights: Most Important Problem] is negatively

correlated with opposition to school and housing or work integration. Again for 1960 and

1964, from the ANES survey, we created dummies (reported in the second and third row of

69More details on ANES sampling methodology and data are available at http://www.electionstudies.org/wp-content/uploads/2018/04/nes012492.pdf

70We create dummies for: high school dropouts; high school graduates; having at least some college; havingat least a college degree.

71Unfortunately, for other years, it was not possible to tell whether the respondent identified civil rights assomething to promote or instead as an issue that was undesirable to her.

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Table D.1) equal to one if the respondent, respectively, agreed that the federal government

should not intervene to promote racial integration in schools and disagreed with the idea that

the government should promote racial integration in housing and labor markets.

As discussed in the main text, we exploit ANES questions concerning political preferences

in surveys in years between 1956 and 1964. In particular, individuals were asked to indicate

the party they had voted (resp. intended to vote) in the previous (resp. upcoming) elections.

From this variable, we create a dummy equal to one if respondents answered that they voted

or intended to vote for the Democratic Party (Vote Democratic). Similarly, the ANES asked

respondents whether they identified with either the Democratic or the Republican Party. As

for voting behavior, we create a dummy equal to one if respondents identified themselves with

the Democratic Party (Identify Democratic). We use these outcomes in the analysis reported

in Table A.19.

Finally, for 1964 only, ANES respondents were asked about their feeling thermometers

towards different political and socio-demographic groups, including the Democratic Party, labor

unions, Blacks and the NAACP. Thermometer values are such that higher values refer to

warmer feelings towards members of the group.72 We use the answers given by respondents in

Appendix Table A.24.

D.2 Gallup

We validate the results obtained using the ANES with Gallup, which elicited respondents’

views about salient political and social issues since 1935.73 As for the ANES, also Gallup is

a repeated cross-sectional dataset from which individual level characteristics are available.74

Starting from the mid-1950s, Gallup asked questions about racial attitudes. As discussed

extensively in Kuziemko and Washington (2018), Gallup data have been only recently made

available due to the efforts of the Roper Center, which digitized hundreds of historical surveys.75

As for the ANES, we restricted attention to white respondents living in non-southern states in

years before 1965. In practice, so as to keep the sample consistent across questions, we focused

on years 1963 and 1964, when different questions, comparable to those from the ANES, were

asked. We report the wording and the survey years for which these two questions are available

in Panel B of Table D.1.

Starting from the top of Panel B, Gallup respondents who had at least one child in school

were asked whether they would object to send their kids to a school with few, half, and more

than half Black pupils. Parents who responded that they would not object to sending their

kids to a school with few Black students were subsequently asked if they would object to a

situation with half Black pupils in the school. If they had no objections to such question, they

were asked about a situation in which the school was more than half Black. Most parents

72The ANES asked respondents about their feeling thermometers towards the two parties also in years otherthan 1964. However, since we are interested in studying whites’ racial attitudes, we limit our analysis to 1964,i.e. the only year for which both political and racial groups or organizations were included.

73See also https://ropercenter.cornell.edu/featured-collections/gallup-data-collection.74With the exception of union membership, marital status, and state of birth, all individual characteristics

available in the ANES (see Appendix D.1) are available for Gallup as well.75More information about Roper Center data can be found here: https://ropercenter.cornell.edu/. We

thank Kathleen Joyce Weldon for invaluable help in the data collection and data cleaning process.

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(90%) did not object to send their kids to schools with only a few Black pupils. Instead, more

heterogeneity existed when parents were asked about a situation in which half or more than

half of the school were racially mixed. Specifically, 30% of parents who did not object to send a

kid to a school that had few Black pupils were against sending their kid to a school where half

of the pupils were Black. Of those that did not object to send their kid to a school where half

of the pupils were Black 38% were against a situation in which more than half of the pupils in

the school were Black.

Given these patterns, we decided to focus on the answer to the scenario in which half

of the pupils in the school were Black. In our view, and consistent with existing evidence

(Sugrue, 2008, 2014), racial mixing was not perceived as a threat when (school or neighborhood)

integration entailed only a limited number of Blacks. Instead, racial animosity and whites’

backlash was more likely to emerge as the share of Blacks in the local (white) community

increased. The variable 1[Object to Half Black Pupils in School] at the top of column 1 in

Table A.20 is thus a dummy equal to 1 if parents did object to sending their kids to a school

with at least half of students being Black.76

The second question used in our analysis is meant to capture whites respondents’ acceptance

of racial diversity in politics. Specifically, as in Kuziemko and Washington (2018), we consider

whether respondents would vote for a Black candidate had their party nominated the individual

for the Presidential race (see second row in Panel B of Table D.1).77 In column 2 of Table A.20,

we create a dummy equal to one if respondents answered that they would vote for a Black

candidate, 1[Vote for Black Candidate].78

In 1964, given the prominence of the issue, Gallup questionnaires included a question about

the Civil Rights Act (CRA). Among the about 1,000 respondents, approximately 70% of them

did approve the law just passed by Congress. We create a dummy equal to one if a respondent

supported the CRA (Approve Civil Rights Act in Panel B of Table D.1). This variable is

considered as outcome in column 3 of Table A.20.

Finally, we consider a question that elicits respondents’ view on how the Kennedy Admin-

istration was handling the process of racial integration. Specifically, we create a dummy equal

to one if an individual stated that in her view, racial integration was proceeding “at the right

pace or not fast enough” (see the last row in Table D.1, Panel B). This variable is used as

outcome in column 4 of Table A.20.79

76The sample size is relatively small – 851 respondents – since only parents with kids in school were askedthis question.

77In 1963 this question was asked to around 2,000 respondents.78Kuziemko and Washington (2018) investigate whites’ respondents to this question also for years after 1965.

Instead, in order not to confound our results with potential whites’ backlash we stop in 1963 – the last yearbefore the passage of the CRA.

79As it appears from Table A.20, this question is available for a significantly larger number of respondents(more than 17,000) relative to all other questions. This is because the question was asked repeatedly in 1963.

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Table D.1. Questions from Survey Data

Variable Name Wording Years

Panel A. ANES

Most Important Problem What would you personally feel are the most important problems the government should tryto take care of when the new president and congress take office in January. (Do you think ofany other problems important to you)

1960 and1964

Against School Integration The government in Washington should stay out of the question of whether white and [Black]children go to the same school.

1960 and1964

Against Work and ResidentialIntegration

If [Blacks] are not getting fair treatment in jobs and housing, the government should see to itthat they do.

1960 and1964

Vote Democratic 1 if voted/intend to vote for the Democratic Party in the last/upcoming Presidential Elections 1956-1964

Party Identification Was there ever a time when you though of yourself as a Democrat or a Republican? 1956-1964

Feeling Thermometer Towards[Group]

There are many groups in America that try to get the government of the American people tosee things more their way. We would like to get your feelings toward some of these groups...Where would you put (group) on the thermometer?

1964

Panel B. Gallup

Object to Half Black Pupils inSchool

Would you, yourself, have any objection to sending your children to a school where half of thechildren are [Black]

1963

Black Candidate There’s always much discussion about the qualifications of presidential candidates - theireducation, age, religion, race and the like... If your party nominated a generally well-qualifiedman for president and he happened to be a [Black] would you vote for him

1963

Approve Civil Rights Act As you know, a civil rights law was recently passed by Congress and signed by the President.In general, do you approve or disapprove this law?

1964

Racial Integration at the RightPace/Not Fast Enough

Do you think the Kennedy Administration is pushing racial integration too fast or not fastenough?

1963

Notes: Panel A (resp. B) lists variables and questions taken from the ANES (Gallup). The wording reported for variables Most Important Problem, Against SchoolIntegration, and Against Work and Residential Integration in Panel A is taken from the 1960 survey, but remains almost identical in all other years considered.

116